problem solving in ai

Problem-Solving Agents In Artificial Intelligence

In artificial intelligence, a problem-solving agent refers to a type of intelligent agent designed to address and solve complex problems or tasks in its environment. These agents are a fundamental concept in AI and are used in various applications, from game-playing algorithms to robotics and decision-making systems. Here are some key characteristics and components of a problem-solving agent:

• Perception : Problem-solving agents typically have the ability to perceive or sense their environment. They can gather information about the current state of the world, often through sensors, cameras, or other data sources.
• Knowledge Base : These agents often possess some form of knowledge or representation of the problem domain. This knowledge can be encoded in various ways, such as rules, facts, or models, depending on the specific problem.
• Reasoning : Problem-solving agents employ reasoning mechanisms to make decisions and select actions based on their perception and knowledge. This involves processing information, making inferences, and selecting the best course of action.
• Planning : For many complex problems, problem-solving agents engage in planning. They consider different sequences of actions to achieve their goals and decide on the most suitable action plan.
• Actuation : After determining the best course of action, problem-solving agents take actions to interact with their environment. This can involve physical actions in the case of robotics or making decisions in more abstract problem-solving domains.
• Feedback : Problem-solving agents often receive feedback from their environment, which they use to adjust their actions and refine their problem-solving strategies. This feedback loop helps them adapt to changing conditions and improve their performance.
• Learning : Some problem-solving agents incorporate machine learning techniques to improve their performance over time. They can learn from experience, adapt their strategies, and become more efficient at solving similar problems in the future.

Problem-solving agents can vary greatly in complexity, from simple algorithms that solve straightforward puzzles to highly sophisticated AI systems that tackle complex, real-world problems. The design and implementation of problem-solving agents depend on the specific problem domain and the goals of the AI application.

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

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AI accelerates problem-solving in complex scenarios

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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How Does Artificial Intelligence Solve Problems? An In-Depth Look at Problem Solving in AI

What is problem solving in artificial intelligence? It is a complex process of finding solutions to challenging problems using computational algorithms and techniques. Artificial intelligence, or AI, refers to the development of intelligent systems that can perform tasks typically requiring human intelligence.

Solving problems in AI involves the use of various algorithms and models that are designed to mimic human cognitive processes. These algorithms analyze and interpret data, generate possible solutions, and evaluate the best course of action. Through machine learning and deep learning, AI systems can continuously improve their problem-solving abilities.

Artificial intelligence problem solving is not limited to a specific domain or industry. It can be applied in various fields such as healthcare, finance, manufacturing, and transportation. AI-powered systems can analyze vast amounts of data, identify patterns, and make predictions to solve complex problems efficiently.

Understanding and developing problem-solving capabilities in artificial intelligence is crucial for the advancement of AI technologies. By improving problem-solving algorithms and models, researchers and developers can create more efficient and intelligent AI systems that can address real-world challenges and contribute to technological progress.

What is Artificial Intelligence?

Artificial intelligence (AI) can be defined as the simulation of human intelligence in machines that are programmed to think and learn like humans. It is a branch of computer science that deals with the creation and development of intelligent machines that can perform tasks that normally require human intelligence.

AI is achieved through the use of algorithms and data that allow machines to learn from and adapt to new information. These machines can then use their knowledge and reasoning abilities to solve problems, make decisions, and even perform tasks that were previously thought to require human intelligence.

Types of Artificial Intelligence

There are two main types of AI: narrow or weak AI and general or strong AI.

Narrow AI refers to AI systems that are designed to perform specific tasks, such as language translation, image recognition, or playing chess. These systems are trained to excel in their specific tasks but lack the ability to generalize their knowledge to other domains.

General AI, on the other hand, refers to AI systems that have the ability to understand, learn, and apply knowledge across a wide range of tasks and domains. These systems are capable of reasoning, problem-solving, and adapting to new situations in a way that is similar to human intelligence.

The Role of Problem Solving in Artificial Intelligence

Problem solving is a critical component of artificial intelligence. It involves the ability of AI systems to identify problems, analyze information, and develop solutions to those problems. AI algorithms are designed to imitate human problem-solving techniques, such as searching for solutions, evaluating options, and making decisions based on available information.

AI systems use various problem-solving techniques, including algorithms such as search algorithms, heuristic algorithms, and optimization algorithms, to find the best solution to a given problem. These techniques allow AI systems to solve complex problems efficiently and effectively.

In conclusion, artificial intelligence is the field of study that focuses on creating intelligent machines that can perform tasks that normally require human intelligence. Problem-solving is a fundamental aspect of AI and involves the use of algorithms and data to analyze information and develop solutions. AI has the potential to revolutionize many aspects of our lives, from healthcare and transportation to business and entertainment.

Problem solving is a critical component of artificial intelligence (AI). AI systems are designed to solve complex, real-world problems by employing various problem-solving techniques and algorithms.

One of the main goals of AI is to create intelligent systems that can solve problems in a way that mimics human problem-solving abilities. This involves using algorithms to search through a vast amount of data and information to find the most optimal solution.

Problem solving in AI involves breaking down a problem into smaller, more manageable sub-problems. These sub-problems are then solved individually and combined to solve the larger problem at hand. This approach allows AI systems to tackle complex problems that would be impossible for a human to solve manually.

AI problem-solving techniques can be classified into two main categories: algorithmic problem-solving and heuristic problem-solving. Algorithmic problem-solving involves using predefined rules and algorithms to solve a problem. These algorithms are based on logical reasoning and can be programmed into AI systems to provide step-by-step instructions for solving a problem.

Heuristic problem-solving, on the other hand, involves using heuristics or rules of thumb to guide the problem-solving process. Heuristics are not guaranteed to find the optimal solution, but they can provide a good enough solution in a reasonable amount of time.

Problem solving in AI is not limited to just finding a single solution to a problem. AI systems can also generate multiple solutions and evaluate them based on predefined criteria. This allows AI systems to explore different possibilities and find the best solution among them.

In conclusion, problem solving is a fundamental aspect of artificial intelligence. AI systems use problem-solving techniques and algorithms to tackle complex real-world problems. Through algorithmic and heuristic problem solving, AI systems are able to find optimal solutions and generate multiple solutions for evaluation. As AI continues to advance, problem-solving abilities will play an increasingly important role in the development of intelligent systems.

Problem Solving Approaches in Artificial Intelligence

In the field of artificial intelligence, problem solving is a fundamental aspect. Artificial intelligence (AI) is the intelligence exhibited by machines or computer systems. It aims to mimic human intelligence in solving complex problems that require reasoning and decision-making.

What is problem solving?

Problem solving refers to the cognitive mental process of finding solutions to difficult or complex issues. It involves identifying the problem, gathering relevant information, analyzing possible solutions, and selecting the most effective one. Problem solving is an essential skill for both humans and AI systems to achieve desired goals.

Approaches in problem solving in AI

Artificial intelligence employs various approaches to problem solving. Some of the commonly used approaches are:

• Search algorithms: These algorithms explore a problem space to find a solution. They can use different search strategies such as depth-first search, breadth-first search, and heuristic search.
• Knowledge-based systems: These systems store and utilize knowledge to solve problems. They rely on rules, facts, and heuristics to guide their problem-solving process.
• Logic-based reasoning: This approach uses logical reasoning to solve problems. It involves representing the problem as a logical formula and applying deduction rules to reach a solution.
• Machine learning: Machine learning algorithms enable AI systems to learn from data and improve their problem-solving capabilities. They can analyze patterns, make predictions, and adjust their behavior based on feedback.

Each approach has its strengths and weaknesses, and the choice of approach depends on the problem domain and available resources. By combining these approaches, AI systems can effectively tackle complex problems and provide valuable solutions.

Search Algorithms in Problem Solving

Problem solving is a critical aspect of artificial intelligence, as it involves the ability to find a solution to a given problem or goal. Search algorithms play a crucial role in problem solving by systematically exploring the search space to find an optimal solution.

What is a Problem?

A problem in the context of artificial intelligence refers to a task or challenge that requires a solution. It can be a complex puzzle, a decision-making problem, or any situation that requires finding an optimal solution.

What is an Algorithm?

An algorithm is a step-by-step procedure or set of rules for solving a problem. In the context of search algorithms, it refers to the systematic exploration of the search space, where each step narrows down the possibilities to find an optimal solution.

Search algorithms in problem solving aim to efficiently explore the search space to find a solution. There are several types of search algorithms, each with its own characteristics and trade-offs.

One commonly used search algorithm is the Breadth-First Search (BFS) algorithm. BFS explores the search space by systematically expanding all possible paths from the initial state to find the goal state. It explores the search space in a breadth-first manner, meaning that it visits all nodes at the same depth level before moving to the next level.

Another popular search algorithm is the Depth-First Search (DFS) algorithm. Unlike BFS, DFS explores the search space by diving deep into a path until it reaches a dead-end or the goal state. It explores the search space in a depth-first manner, meaning that it explores the deepest paths first before backtracking.

Other search algorithms include the A* algorithm, which combines the efficiency of BFS with the heuristic guidance of algorithms; the Greedy Best-First Search, which prioritizes paths based on a heuristic evaluation; and the Hill Climbing algorithm, which iteratively improves the current solution by making small changes.

Search algorithms in problem solving are essential in the field of artificial intelligence as they enable systems to find optimal solutions efficiently. By understanding and implementing different search algorithms, developers and researchers can design intelligent systems capable of solving complex problems.

Heuristic Functions in Problem Solving

In the field of artificial intelligence, problem-solving is a crucial aspect of creating intelligent systems. One key component in problem-solving is the use of heuristic functions.

A heuristic function is a function that guides an intelligent system in making decisions about how to solve a problem. It provides an estimate of the best possible solution based on available information at any given point in the problem-solving process.

What is a Heuristic Function?

A heuristic function is designed to provide a quick, yet informed, estimate of the most promising solution out of a set of possible solutions. It helps the intelligent system prioritize its search and focus on the most likely path to success.

Heuristic functions are especially useful in problems that have a large number of possible solutions and where an exhaustive search through all possibilities would be impractical or inefficient.

How Does a Heuristic Function Work?

Heuristic functions take into account various factors and considerations that are relevant to the problem being solved. These factors could include knowledge about the problem domain, past experience, or rules and constraints specific to the problem.

The heuristic function assigns a value to each possible solution based on these factors. The higher the value, the more likely a solution is to be optimal. The intelligent system then uses this information to guide its search for the best solution.

A good heuristic function strikes a balance between accuracy and efficiency. It should be accurate enough to guide the search towards the best solution but should also be computationally efficient to prevent excessive computation time.

Overall, heuristic functions play a crucial role in problem-solving in artificial intelligence. They provide a way for intelligent systems to efficiently navigate complex problem domains and find near-optimal solutions.

Constraint Satisfaction in Problem Solving

Problem solving is a key component of artificial intelligence, as it involves using computational methods to find solutions to complex issues. However, understanding how to solve these problems efficiently is essential for developing effective AI systems. And this is where constraint satisfaction comes into play.

Constraint satisfaction is a technique used in problem solving to ensure that all solution candidates satisfy a set of predefined constraints. These constraints can be thought of as rules or conditions that must be met for a solution to be considered valid.

So, what is a constraint? A constraint is a limitation or restriction on the values that variables can take. For example, in a scheduling problem, constraints can include time availability, resource limitations, or precedence relationships between tasks.

The goal of constraint satisfaction in problem-solving is to find a solution that satisfies all the given constraints. This is achieved by exploring the space of possible solutions and eliminating those that violate the constraints.

Constraint satisfaction problems (CSPs) can be solved using various algorithms, such as backtracking or constraint propagation. These algorithms iteratively assign values to variables and check if the constraints are satisfied. If a constraint is violated, the algorithm backtracks and tries a different value for the previous variable.

One advantage of using constraint satisfaction in problem solving is that it provides a systematic way to represent and solve problems with complex constraints. By breaking down the problem into smaller constraints, it becomes easier to reason about the problem and find a solution.

In conclusion, constraint satisfaction is an important technique in problem solving for artificial intelligence. By defining and enforcing constraints, AI systems can efficiently search for valid solutions. Incorporating constraint satisfaction techniques into AI algorithms can greatly improve problem-solving capabilities and contribute to the development of more intelligent systems.

Genetic Algorithms in Problem Solving

Artificial intelligence (AI) is a branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. One aspect of AI is problem solving, which involves finding solutions to complex problems. Genetic algorithms are a type of problem-solving method used in artificial intelligence.

So, what are genetic algorithms? In simple terms, genetic algorithms are inspired by the process of natural selection and evolution. They are a type of optimization algorithm that uses concepts from genetics and biology to find the best solution to a problem. Instead of relying on a predefined set of rules or instructions, genetic algorithms work by evolving a population of potential solutions over multiple generations.

The process of genetic algorithms involves several key steps. First, an initial population of potential solutions is generated. Each solution is represented as a set of variables or “genes.” These solutions are then evaluated based on their fitness or how well they solve the problem at hand.

Next, the genetic algorithm applies operators such as selection, crossover, and mutation to the current population. Selection involves choosing the fittest solutions to become the parents for the next generation. Crossover involves combining the genes of two parents to create offspring with a mix of their characteristics. Mutation introduces small random changes in the offspring’s genes to introduce genetic diversity.

The new population is then evaluated, and the process continues until a stopping criterion is met, such as finding a solution that meets a certain fitness threshold or reaching a maximum number of generations. Over time, the genetic algorithm converges towards the best solution, much like how natural selection leads to the evolution of species.

Genetic algorithms have been successfully applied to a wide range of problem-solving tasks, including optimization, machine learning, and scheduling. They have been used to solve problems in areas such as engineering, finance, and biology. Due to their ability to explore a large solution space and find globally optimal or near-optimal solutions, genetic algorithms are often preferred when traditional methods fail or are not feasible.

In conclusion, genetic algorithms are a powerful tool in the field of artificial intelligence and problem solving. By mimicking the process of natural selection and evolution, they provide a way to find optimal solutions to complex problems. Their ability to explore a wide search space and adapt to changing environments makes them well-suited for a variety of problem-solving tasks. As AI continues to advance, genetic algorithms will likely play an increasingly important role in solving real-world problems.

Logical Reasoning in Problem Solving

Problem solving is a fundamental aspect of artificial intelligence. It involves finding a solution to a given problem by using logical reasoning. Logical reasoning is the process of using valid arguments and deductions to make inferences and arrive at a logical conclusion. In the context of problem solving, logical reasoning is used to analyze the problem, identify potential solutions, and evaluate their feasibility.

Logical reasoning is what sets artificial intelligence apart from other problem-solving approaches. Unlike human problem solvers, AI can analyze vast amounts of data and consider numerous possibilities simultaneously. It can also distinguish between relevant and irrelevant information and use it to make informed decisions.

Types of Logical Reasoning

There are several types of logical reasoning that AI systems employ in problem solving:

• Deductive Reasoning: Deductive reasoning involves drawing specific conclusions from general principles or premises. It uses a top-down approach, starting from general knowledge and applying logical rules to derive specific conclusions.
• Inductive Reasoning: Inductive reasoning involves drawing general conclusions or patterns from specific observations or examples. It uses a bottom-up approach, where specific instances are used to make generalizations.
• Abductive Reasoning: Abductive reasoning involves making the best possible explanation or hypothesis based on the available evidence. It is a form of reasoning that combines deductive and inductive reasoning to generate the most likely conclusion.

Importance of Logical Reasoning in Problem Solving

Logical reasoning is crucial in problem solving as it ensures that the solutions generated by AI systems are sound, valid, and reliable. Without logical reasoning, AI systems may produce incorrect or nonsensical solutions that are of no use in practical applications.

Furthermore, logical reasoning helps AI systems analyze complex problems systematically and break them down into smaller, more manageable sub-problems. By applying logical rules and deductions, AI systems can generate possible solutions, evaluate their feasibility, and select the most optimal one.

In conclusion, logical reasoning plays a vital role in problem solving in artificial intelligence. It enables AI systems to analyze problems, consider multiple possibilities, and arrive at logical conclusions. By employing various types of logical reasoning, AI systems can generate accurate and effective solutions to a wide range of problems.

Planning and Decision Making in Problem Solving

Planning and decision making play crucial roles in the field of artificial intelligence when it comes to problem solving . A fundamental aspect of problem solving is understanding what the problem actually is and how it can be solved.

Planning refers to the process of creating a sequence of actions or steps to achieve a specific goal. In the context of artificial intelligence, planning involves creating a formal representation of the problem and finding a sequence of actions that will lead to a solution. This can be done by using various techniques and algorithms, such as heuristic search or constraint satisfaction.

Decision making, on the other hand, is the process of selecting the best course of action among several alternatives. In problem solving, decision making is essential at every step, from determining the initial state to selecting the next action to take. Decision making is often based on evaluation and comparison of different options, taking into consideration factors such as feasibility, cost, efficiency, and the desired outcome.

Both planning and decision making are closely intertwined in problem solving. Planning helps in breaking down a problem into smaller, manageable sub-problems and devising a strategy to solve them. Decision making, on the other hand, guides the selection of actions or steps at each stage of the problem-solving process.

In conclusion, planning and decision making are integral components of the problem-solving process in artificial intelligence. Understanding the problem at hand, creating a plan, and making informed decisions are essential for achieving an effective and efficient solution.

Challenges in Problem Solving in Artificial Intelligence

Problem solving is at the core of what artificial intelligence is all about. It involves using intelligent systems to find solutions to complex problems, often with limited information or resources. While artificial intelligence has made great strides in recent years, there are still several challenges that need to be overcome in order to improve problem solving capabilities.

Limited Data and Information

One of the main challenges in problem solving in artificial intelligence is the availability of limited data and information. Many problems require a large amount of data to be effective, but gathering and organizing that data can be time-consuming and difficult. Additionally, there may be cases where the necessary data simply doesn’t exist, making it even more challenging to find a solution.

Complexity and Uncertainty

Another challenge is the complexity and uncertainty of many real-world problems. Artificial intelligence systems need to be able to handle ambiguous, incomplete, or contradictory information in order to find appropriate solutions. This requires advanced algorithms and models that can handle uncertainty and make decisions based on probabilistic reasoning.

Intelligent Decision-Making

In problem solving, artificial intelligence systems need to be able to make intelligent decisions based on the available information. This involves understanding the problem at hand, identifying potential solutions, and evaluating the best course of action. Intelligent decision-making requires not only advanced algorithms but also the ability to learn from past experiences and adapt to new situations.

In conclusion, problem solving in artificial intelligence is a complex and challenging task. Limited data and information, complexity and uncertainty, and the need for intelligent decision-making are just a few of the challenges that need to be addressed. However, with continued research and advancement in the field, it is hoped that these challenges can be overcome, leading to even more effective problem solving in artificial intelligence.

Complexity of Problems

Artificial intelligence (AI) is transforming many aspects of our lives, including problem solving. But what exactly is the complexity of the problems that AI is capable of solving?

The complexity of a problem refers to the level of difficulty involved in finding a solution. In the context of AI, it often refers to the computational complexity of solving a problem using algorithms.

AI is known for its ability to handle complex problems that would be difficult or time-consuming for humans to solve. This is because AI can process and analyze large amounts of data quickly, allowing it to explore different possibilities and find optimal solutions.

One of the key factors that determines the complexity of a problem is the size of the problem space. The problem space refers to the set of all possible states or configurations of a problem. The larger the problem space, the more complex the problem is.

Another factor that influences the complexity of a problem is the nature of the problem itself. Some problems are inherently more difficult to solve than others. For example, problems that involve combinatorial optimization or probabilistic reasoning are often more complex.

Furthermore, the complexity of a problem can also depend on the available resources and the algorithms used to solve it. Certain problems may require significant computational power or specialized algorithms to find optimal solutions.

In conclusion, the complexity of problems that AI is capable of solving is determined by various factors, including the size of the problem space, the nature of the problem, and the available resources. AI’s ability to handle complex problems is one of the key reasons why it is transforming many industries and becoming an essential tool in problem solving.

Incomplete or Uncertain Information

One of the challenges in problem solving in artificial intelligence is dealing with incomplete or uncertain information. In many real-world scenarios, AI systems have to make decisions based on incomplete or uncertain knowledge. This can happen due to various reasons, such as missing data, conflicting information, or uncertain predictions.

When faced with incomplete information, AI systems need to rely on techniques that can handle uncertainty. One such technique is probabilistic reasoning, which allows AI systems to assign probabilities to different possible outcomes and make decisions based on these probabilities. By using probabilistic models, AI systems can estimate the most likely outcomes and use this information to guide problem-solving processes.

In addition to probabilistic reasoning, AI systems can also utilize techniques like fuzzy logic and Bayesian networks to handle incomplete or uncertain information. Fuzzy logic allows for the representation and manipulation of uncertain or vague concepts, while Bayesian networks provide a graphical representation of uncertain relationships between variables.

Overall, dealing with incomplete or uncertain information is an important aspect of problem solving in artificial intelligence. AI systems need to be equipped with techniques and models that can handle uncertainty and make informed decisions based on incomplete or uncertain knowledge. By incorporating these techniques, AI systems can overcome limitations caused by incomplete or uncertain information and improve problem-solving capabilities.

Dynamic Environments

In the field of artificial intelligence, problem solving is a fundamental task. However, in order to solve a problem, it is important to understand what the problem is and what intelligence is required to solve it.

What is a problem?

A problem can be defined as a situation in which an individual or system faces a challenge and needs to find a solution. Problems can vary in complexity and can be static or dynamic in nature.

What is dynamic intelligence?

Dynamic intelligence refers to the ability of an individual or system to adapt and respond to changing environments or situations. In the context of problem solving in artificial intelligence, dynamic environments play a crucial role.

In dynamic environments, the problem or the conditions surrounding the problem can change over time. This requires the problem-solving system to be able to adjust its approach or strategy in order to find a solution.

Dynamic environments can be found in various domains, such as robotics, autonomous vehicles, and game playing. For example, in a game, the game board or the opponent’s moves can change, requiring the player to adapt their strategy.

To solve problems in dynamic environments, artificial intelligence systems need to possess the ability to perceive changes, learn from past experiences, and make decisions based on the current state of the environment.

In conclusion, understanding dynamic environments is essential for problem solving in artificial intelligence. By studying how intelligence can adapt and respond to changing conditions, researchers can develop more efficient and effective problem-solving algorithms.

Optimization vs. Satisficing

In the field of artificial intelligence and problem solving, there are two main approaches: optimization and satisficing. These approaches differ in their goals and strategies for finding solutions to problems.

What is optimization?

Optimization is the process of finding the best solution to a problem, typically defined as maximizing or minimizing a certain objective function. In the context of artificial intelligence, this often involves finding the optimal values for a set of variables that satisfy a given set of constraints. The goal is to find the solution that maximizes or minimizes the objective function while satisfying all the constraints. Optimization algorithms, such as gradient descent or genetic algorithms, are often used to search for the best solution.

What is satisficing?

Satisficing, on the other hand, focuses on finding solutions that are good enough to meet a certain set of criteria or requirements. The goal is not to find the absolute best solution, but rather to find a solution that satisfies a sufficient level of performance. Satisficing algorithms often trade off between the quality of the solution and the computational resources required to find it. These algorithms aim to find a solution that meets the requirements while minimizing the computational effort.

Both optimization and satisficing have their advantages and disadvantages. Optimization is typically used when the problem has a clear objective function and the goal is to find the best possible solution. However, it can be computationally expensive and time-consuming, especially for complex problems. Satisficing, on the other hand, is often used when the problem is ill-defined or there are multiple conflicting objectives. It allows for faster and less resource-intensive solutions, but the quality of the solution may be compromised to some extent.

In conclusion, the choice between optimization and satisficing depends on the specific problem at hand and the trade-offs between the desired solution quality and computational resources. Understanding these approaches can help in developing effective problem-solving strategies in the field of artificial intelligence.

Ethical Considerations in Problem Solving

Intelligence is the ability to understand and learn from experiences, solve problems, and adapt to new situations. Artificial intelligence (AI) is a field that aims to develop machines and algorithms that possess these abilities. Problem solving is a fundamental aspect of intelligence, as it involves finding solutions to challenges and achieving desired outcomes.

The Role of Ethics

However, it is essential to consider the ethical implications of problem solving in the context of AI. What is considered a suitable solution for a problem and how it is obtained can have significant ethical consequences. AI systems and algorithms should be designed in a way that promotes fairness, transparency, and accountability.

Fairness: AI systems should not discriminate against any individuals or groups based on characteristics such as race, gender, or religion. The solutions generated should be fair and unbiased, taking into account diverse perspectives and circumstances.

Transparency: AI algorithms should be transparent in their decision-making process. The steps taken to arrive at a solution should be understandable and explainable, enabling humans to assess the algorithm’s reliability and correctness.

The Impact of AI Problem Solving

Problem solving in AI can have various impacts, both positive and negative, on individuals and society as a whole. AI systems can help address complex problems and make processes more efficient, leading to advancements in fields such as healthcare, transportation, and finance.

On the other hand, there can be ethical concerns regarding the use of AI in problem solving:

– Privacy: AI systems may collect and analyze vast amounts of data, raising concerns about privacy invasion and potential misuse of personal information.

– Job displacement: As AI becomes more capable of problem solving, there is a possibility of job displacement for certain professions. It is crucial to consider the societal impact and explore ways to mitigate the negative effects.

In conclusion, ethical considerations play a vital role in problem solving in artificial intelligence. It is crucial to design AI systems that are fair, transparent, and accountable. Balancing the potential benefits of AI problem solving with its ethical implications is necessary to ensure the responsible and ethical development of AI technologies.

Question-answer:

What is problem solving in artificial intelligence.

Problem solving in artificial intelligence refers to the process of finding solutions to complex problems using computational systems or algorithms. It involves defining and structuring the problem, formulating a plan or strategy to solve it, and executing the plan to reach the desired solution.

What are the steps involved in problem solving in artificial intelligence?

The steps involved in problem solving in artificial intelligence typically include problem formulation, creating a search space, search strategy selection, executing the search, and evaluating the solution. Problem formulation involves defining the problem and its constraints, while creating a search space involves representing all possible states and actions. The search strategy selection determines the approach used to explore the search space, and executing the search involves systematically exploring the space to find a solution. Finally, the solution is evaluated based on predefined criteria.

What are some common techniques used for problem solving in artificial intelligence?

There are several common techniques used for problem solving in artificial intelligence, including uninformed search algorithms (such as breadth-first search and depth-first search), heuristic search algorithms (such as A* search), constraint satisfaction algorithms, and machine learning algorithms. Each technique has its own advantages and is suited for different types of problems.

Can problem solving in artificial intelligence be applied to real-world problems?

Yes, problem solving in artificial intelligence can be applied to real-world problems. It has been successfully used in various domains, such as robotics, healthcare, finance, and transportation. By leveraging computational power and advanced algorithms, artificial intelligence can provide efficient and effective solutions to complex problems.

What are the limitations of problem solving in artificial intelligence?

Problem solving in artificial intelligence has certain limitations. It heavily relies on the quality of input data and the accuracy of algorithms. In cases where the problem space is vast and complex, finding an optimal solution may be computationally expensive or even infeasible. Additionally, problem solving in artificial intelligence may not always capture human-like reasoning and may lack common sense knowledge, which can limit its ability to solve certain types of problems.

Problem solving in artificial intelligence is the process of finding solutions to complex problems using computer algorithms. It involves using various techniques and methods to analyze a problem, break it down into smaller sub-problems, and then develop a step-by-step approach to solving it.

How does artificial intelligence solve problems?

Artificial intelligence solves problems by employing different algorithms and approaches. These include search algorithms, heuristic methods, constraint satisfaction techniques, genetic algorithms, and machine learning. The choice of the specific algorithms depends on the nature of the problem and the available data.

What are the steps involved in problem solving using artificial intelligence?

The steps involved in problem solving using artificial intelligence typically include problem analysis, formulation, search or exploration of possible solutions, evaluation of the solutions, and finally, selecting the best solution. These steps may be repeated iteratively until a satisfactory solution is found.

What are some real-life applications of problem solving in artificial intelligence?

Problem solving in artificial intelligence has various real-life applications. It is used in areas such as robotics, natural language processing, computer vision, data analysis, expert systems, and autonomous vehicles. For example, self-driving cars use problem-solving techniques to navigate and make decisions on the road.

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Chapter 3 Solving Problems by Searching 

When the correct action to take is not immediately obvious, an agent may need to plan ahead : to consider a sequence of actions that form a path to a goal state. Such an agent is called a problem-solving agent , and the computational process it undertakes is called search .

Problem-solving agents use atomic representations, that is, states of the world are considered as wholes, with no internal structure visible to the problem-solving algorithms. Agents that use factored or structured representations of states are called planning agents .

We distinguish between informed algorithms, in which the agent can estimate how far it is from the goal, and uninformed algorithms, where no such estimate is available.

3.1 Problem-Solving Agents 

If the agent has no additional information—that is, if the environment is unknown —then the agent can do no better than to execute one of the actions at random. For now, we assume that our agents always have access to information about the world. With that information, the agent can follow this four-phase problem-solving process:

GOAL FORMULATION : Goals organize behavior by limiting the objectives and hence the actions to be considered.

PROBLEM FORMULATION : The agent devises a description of the states and actions necessary to reach the goal—an abstract model of the relevant part of the world.

SEARCH : Before taking any action in the real world, the agent simulates sequences of actions in its model, searching until it finds a sequence of actions that reaches the goal. Such a sequence is called a solution .

EXECUTION : The agent can now execute the actions in the solution, one at a time.

It is an important property that in a fully observable, deterministic, known environment, the solution to any problem is a fixed sequence of actions . The open-loop system means that ignoring the percepts breaks the loop between agent and environment. If there is a chance that the model is incorrect, or the environment is nondeterministic, then the agent would be safer using a closed-loop approach that monitors the percepts.

In partially observable or nondeterministic environments, a solution would be a branching strategy that recommends different future actions depending on what percepts arrive.

3.1.1 Search problems and solutions 

A search problem can be defined formally as follows:

A set of possible states that the environment can be in. We call this the state space .

The initial state that the agent starts in.

A set of one or more goal states . We can account for all three of these possibilities by specifying an \(Is\-Goal\) method for a problem.

The actions available to the agent. Given a state \(s\) , \(Actions(s)\) returns a finite set of actions that can be executed in \(s\) . We say that each of these actions is applicable in \(s\) .

A transition model , which describes what each action does. \(Result(s,a)\) returns the state that results from doing action \(a\) in state \(s\) .

An action cost function , denote by \(Action\-Cost(s,a,s\pr)\) when we are programming or \(c(s,a,s\pr)\) when we are doing math, that gives the numeric cost of applying action \(a\) in state \(s\) to reach state \(s\pr\) .

A sequence of actions forms a path , and a solution is a path from the initial state to a goal state. We assume that action costs are additive; that is, the total cost of a path is the sum of the individual action costs. An optimal solution has the lowest path cost among all solutions.

The state space can be represented as a graph in which the vertices are states and the directed edges between them are actions.

3.1.2 Formulating problems 

The process of removing detail from a representation is called abstraction . The abstraction is valid if we can elaborate any abstract solution into a solution in the more detailed world. The abstraction is useful if carrying out each of the actions in the solution is easier than the original problem.

3.2 Example Problems 

A standardized problem is intended to illustrate or exercise various problem-solving methods. It can be given a concise, exact description and hence is suitable as a benchmark for researchers to compare the performance of algorithms. A real-world problem , such as robot navigation, is one whose solutions people actually use, and whose formulation is idiosyncratic, not standardized, because, for example, each robot has different sensors that produce different data.

3.2.1 Standardized problems 

A grid world problem is a two-dimensional rectangular array of square cells in which agents can move from cell to cell.

Vacuum world

Sokoban puzzle

Sliding-tile puzzle

3.2.2 Real-world problems 

Route-finding problem

Touring problems

Trveling salesperson problem (TSP)

VLSI layout problem

Robot navigation

Automatic assembly sequencing

3.3 Search Algorithms 

A search algorithm takes a search problem as input and returns a solution, or an indication of failure. We consider algorithms that superimpose a search tree over the state-space graph, forming various paths from the initial state, trying to find a path that reaches a goal state. Each node in the search tree corresponds to a state in the state space and the edges in the search tree correspond to actions. The root of the tree corresponds to the initial state of the problem.

The state space describes the (possibly infinite) set of states in the world, and the actions that allow transitions from one state to another. The search tree describes paths between these states, reaching towards the goal. The search tree may have multiple paths to (and thus multiple nodes for) any given state, but each node in the tree has a unique path back to the root (as in all trees).

The frontier separates two regions of the state-space graph: an interior region where every state has been expanded, and an exterior region of states that have not yet been reached.

3.3.1 Best-first search 

In best-first search we choose a node, \(n\) , with minimum value of some evaluation function , \(f(n)\) .

3.3.2 Search data structures 

A node in the tree is represented by a data structure with four components

\(node.State\) : the state to which the node corresponds;

\(node.Parent\) : the node in the tree that generated this node;

\(node.Action\) : the action that was applied to the parent’s state to generate this node;

\(node.Path\-Cost\) : the total cost of the path from the initial state to this node. In mathematical formulas, we use \(g(node)\) as a synonym for \(Path\-Cost\) .

Following the \(PARENT\) pointers back from a node allows us to recover the states and actions along the path to that node. Doing this from a goal node gives us the solution.

We need a data structure to store the frontier . The appropriate choice is a queue of some kind, because the operations on a frontier are:

\(Is\-Empty(frontier)\) returns true only if there are no nodes in the frontier.

\(Pop(frontier)\) removes the top node from the frontier and returns it.

\(Top(frontier)\) returns (but does not remove) the top node of the frontier.

\(Add(node, frontier)\) inserts node into its proper place in the queue.

Three kinds of queues are used in search algorithms:

A priority queue first pops the node with the minimum cost according to some evaluation function, \(f\) . It is used in best-first search.

A FIFO queue or first-in-first-out queue first pops the node that was added to the queue first; we shall see it is used in breadth-first search.

A LIFO queue or last-in-first-out queue (also known as a stack ) pops first the most recently added node; we shall see it is used in depth-first search.

3.3.3 Redundant paths 

A cycle is a special case of a redundant path .

As the saying goes, algorithms that cannot remember the past are doomed to repeat it . There are three approaches to this issue.

First, we can remember all previously reached states (as best-first search does), allowing us to detect all redundant paths, and keep only the best path to each state.

Second, we can not worry about repeating the past. We call a search algorithm a graph search if it checks for redundant paths and a tree-like search if it does not check.

Third, we can compromise and check for cycles, but not for redundant paths in general.

3.3.4 Measuring problem-solving performance 

COMPLETENESS : Is the algorithm guaranteed to find a solution when there is one, and to correctly report failure when there is not?

COST OPTIMALITY : Does it find a solution with the lowest path cost of all solutions?

TIME COMPLEXITY : How long does it take to find a solution?

SPACE COMPLEXITY : How much memory is needed to perform the search?

To be complete, a search algorithm must be systematic in the way it explores an infinite state space, making sure it can eventually reach any state that is connected to the initial state.

In theoretical computer science, the typical measure of time and space complexity is the size of the state-space graph, \(|V|+|E|\) , where \(|V|\) is the number of vertices (state nodes) of the graph and \(|E|\) is the number of edges (distinct state/action pairs). For an implicit state space, complexity can be measured in terms of \(d\) , the depth or number of actions in an optimal solution; \(m\) , the maximum number of actions in any path; and \(b\) , the branching factor or number of successors of a node that need to be considered.

3.4 Uninformed Search Strategies 

3.4.1 breadth-first search .

When all actions have the same cost, an appropriate strategy is breadth-first search , in which the root node is expanded first, then all the successors of the root node are expanded next, then their successors, and so on.

Breadth-first search always finds a solution with a minimal number of actions, because when it is generating nodes at depth \(d\) , it has already generated all the nodes at depth \(d-1\) , so if one of them were a solution, it would have been found.

All the nodes remain in memory, so both time and space complexity are \(O(b^d)\) . The memory requirements are a bigger problem for breadth-first search than the execution time . In general, exponential-complexity search problems cannot be solved by uninformed search for any but the smallest instances .

3.4.2 Dijkstra’s algorithm or uniform-cost search 

When actions have different costs, an obvious choice is to use best-first search where the evaluation function is the cost of the path from the root to the current node. This is called Dijkstra’s algorithm by the theoretical computer science community, and uniform-cost search by the AI community.

The complexity of uniform-cost search is characterized in terms of \(C^*\) , the cost of the optimal solution, and \(\epsilon\) , a lower bound on the cost of each action, with \(\epsilon>0\) . Then the algorithm’s worst-case time and space complexity is \(O(b^{1+\lfloor C^*/\epsilon\rfloor})\) , which can be much greater than \(b^d\) .

When all action costs are equal, \(b^{1+\lfloor C^*/\epsilon\rfloor}\) is just \(b^{d+1}\) , and uniform-cost search is similar to breadth-first search.

3.4.3 Depth-first search and the problem of memory 

Depth-first search always expands the deepest node in the frontier first. It could be implemented as a call to \(Best\-First\-Search\) where the evaluation function \(f\) is the negative of the depth.

For problems where a tree-like search is feasible, depth-first search has much smaller needs for memory. A depth-first tree-like search takes time proportional to the number of states, and has memory complexity of only \(O(bm)\) , where \(b\) is the branching factor and \(m\) is the maximum depth of the tree.

A variant of depth-first search called backtracking search uses even less memory.

3.4.4 Depth-limited and iterative deepening search 

To keep depth-first search from wandering down an infinite path, we can use depth-limited search , a version of depth-first search in which we supply a depth limit, \(l\) , and treat all nodes at depth \(l\) as if they had no successors. The time complexity is \(O(b^l)\) and the space complexity is \(O(bl)\)

Iterative deepening search solves the problem of picking a good value for \(l\) by trying all values: first 0, then 1, then 2, and so on—until either a solution is found, or the depth- limited search returns the failure value rather than the cutoff value.

Its memory requirements are modest: \(O(bd)\) when there is a solution, or \(O(bm)\) on finite state spaces with no solution. The time complexity is \(O(bd)\) when there is a solution, or \(O(bm)\) when there is none.

In general, iterative deepening is the preferred uninformed search method when the search state space is larger than can fit in memory and the depth of the solution is not known .

3.4.5 Bidirectional search 

An alternative approach called bidirectional search simultaneously searches forward from the initial state and backwards from the goal state(s), hoping that the two searches will meet.

3.4.6 Comparing uninformed search algorithms 

3.5 Informed (Heuristic) Search Strategies 

An informed search strategy uses domain–specific hints about the location of goals to find colutions more efficiently than an uninformed strategy. The hints come in the form of a heuristic function , denoted \(h(n)\) :

\(h(n)\) = estimated cost of the cheapest path from the state at node \(n\) to a goal state.

3.5.1 Greedy best-first search 

Greedy best-first search is a form of best-first search that expands first the node with the lowest \(h(n)\) value—the node that appears to be closest to the goal—on the grounds that this is likely to lead to a solution quickly. So the evaluation function \(f(n)=h(n)\) .

Understanding AI Problem Formulation Basics

In artificial intelligence, problem formulation is the process of identifying, analyzing, and defining the problems that need to be solved using AI techniques. It involves breaking down complex problems into smaller, more manageable components and formulating a clear problem statement. Problem formulation plays a crucial role in shaping efficient and smart solutions in the field of AI. It helps AI agents define the goals, initial state, actions, transitions, and goal test required to solve a problem. By understanding the basics of problem formulation in AI , you can gain insights into the techniques and algorithms used to solve various problems in artificial intelligence.

• 0.1 Key Takeaways
• 1 The Three Types of Problems in AI
• 2.1 Step 1: Problem Definition
• 2.2 Step 2: Problem Analysis
• 2.3 Step 3: Knowledge Representation
• 2.4 Step 4: Problem-Solving
• 2.5 Step 5: Formulating Associated Problem Components
• 3 Problem-Solving Approaches in AI
• 4 Conclusion
• 5.1 What is problem formulation in artificial intelligence?
• 5.2 What are the three types of problems in AI?
• 5.3 What are the steps for problem solving in AI?
• 5.4 What are the problem-solving approaches in AI?
• 5.5 What is the role of problem formulation and problem solving in AI?
• 6 Source Links

Key Takeaways

• Problem formulation is the process of identifying, analyzing, and defining the problems in artificial intelligence.
• It involves breaking down complex problems into smaller, more manageable components.
• Problem formulation helps AI agents define the goals, initial state, actions, transitions, and goal test required to solve a problem.
• Understanding problem formulation is essential for implementing problem-solving techniques in AI.
• Various techniques and algorithms are used in problem formulation to solve problems in artificial intelligence.

The Three Types of Problems in AI

In the domain of artificial intelligence, there are three types of problems: ignorable, recoverable, and irrecoverable. Understanding these problem types is crucial in determining the appropriate problem-solving techniques and algorithms to be applied.

Ignorable problems are those where certain solution steps can be ignored without affecting the final outcome. These problems usually involve redundant or unnecessary actions that can be omitted in the solution process.

Recoverable problems , on the other hand, are those where solution steps can be undone or reversed if needed. This type of problem allows for flexibility in the problem-solving process, as mistakes or incorrect steps can be rectified along the way.

Irrecoverable problems are the most challenging type, as the solution steps cannot be undone once executed. This means that careful consideration and analysis must be undertaken before taking any action, as there is no turning back.

Examples of problem formulation in artificial intelligence can include tasks such as:

• Pathfinding: Finding the optimal route from one point to another in a given environment.
• Constraint Satisfaction: Satisfying a set of constraints or conditions while searching for a solution.
• Optimization: Maximizing or minimizing a specific objective within a given set of constraints.

By understanding the different types of problems in AI and their corresponding examples, you can approach problem formulation and problem-solving with a clear strategy and direction, optimizing your chances of finding effective solutions.

Steps for Problem Solving in AI

Problem-solving in AI is a multi-step process that allows you to tackle complex problems using various techniques and algorithms. By understanding and following these steps, you can effectively solve problems in the field of artificial intelligence.

Step 1: Problem Definition

Problem definition is the first crucial step in problem-solving. It involves clearly specifying the inputs and acceptable system solutions for the given problem. By defining the problem accurately, you provide a solid foundation for finding the right solution.

Step 2: Problem Analysis

Once the problem is defined, the next step is to analyze it thoroughly. This involves examining the problem from different angles, identifying any patterns or underlying factors, and gaining a deeper understanding of its complexity. Problem analysis helps you uncover valuable insights that can guide your problem-solving approach.

Step 3: Knowledge Representation

Knowledge representation involves collecting detailed information about the problem and exploring possible techniques and algorithms for solving it. By understanding the available resources and methodologies, you can choose the most effective approach to tackle the problem at hand.

Step 4: Problem-Solving

Once you have analyzed the problem and gathered the necessary knowledge, it’s time to apply problem-solving techniques. This step involves selecting the best techniques and algorithms based on the problem’s characteristics and constraints. By using the right tools, you increase the chances of finding an optimal solution.

Step 5: Formulating Associated Problem Components

To achieve the desired goal, it’s crucial to formulate the associated problem components. This includes defining the initial state, actions, transitions, goal test, and path costing required to solve the problem effectively. By carefully formulating these components, you create a structured framework that aids in problem-solving.

Understanding these steps is essential for implementing problem-solving techniques in AI. By following a systematic approach and leveraging the power of AI algorithms, you can overcome complex challenges and find innovative solutions.

Problem-Solving Approaches in AI

In the field of artificial intelligence, there are various approaches to problem-solving. These approaches utilize different algorithms and techniques to tackle complex problems and find effective solutions. Three common problem-solving approaches in AI include heuristic algorithms , searching algorithms , and genetic algorithms .

Heuristic algorithms are used to experiment and test different procedures in order to understand the problem and generate a solution. While they may not always provide the optimal solution, heuristic algorithms offer effective short-term methods for achieving goals. By leveraging prior knowledge and experience, these algorithms can guide problem-solving processes and provide valuable insights.

Searching algorithms are fundamental techniques used by rational agents or problem-solving agents to find the most appropriate solutions. These algorithms involve creating and exploring a search space to identify the desired solution. By systematically traversing the search space, searching algorithms can efficiently navigate through complex problem domains and identify potential solutions. They play a crucial role in solving problems such as pathfinding, constraint satisfaction, and optimization tasks.

Genetic algorithms are inspired by evolutionary theory and natural selection. These algorithms employ a population-based approach and simulate the natural process of evolution to solve problems. By generating and evolving populations of potential solutions, genetic algorithms mimic genetic variation, selection, and reproduction to find optimal or near-optimal solutions. Genetic algorithms are particularly effective in solving complex problems with multiple variables and constraints.

Understanding these problem-solving approaches is essential in selecting the most suitable technique for a given problem in artificial intelligence. Whether utilizing heuristic algorithms , searching algorithms , or genetic algorithms, each approach offers unique benefits and trade-offs. By leveraging these approaches, AI practitioners can develop intelligent systems and applications capable of solving complex problems effectively and efficiently.

Note: The table above summarizes the main characteristics of each problem-solving approach in AI.

Problem formulation is a critical component of problem-solving in artificial intelligence. By identifying, analyzing, and defining the problems that need to be solved using AI techniques, you can lay the foundation for efficient and smart solutions. Through problem formulation, AI agents can define clear goals, initial states, actions, transitions, and goal tests required to achieve the desired outcome.

To successfully solve problems in AI, it is essential to follow the steps for problem-solving in AI. These steps include problem definition, problem analysis, knowledge representation, problem-solving, and formulation of associated problem components. By systematically going through these steps, you can gain a deep understanding of the problem and select the most suitable techniques to solve it.

Various problem-solving approaches, such as heuristic algorithms, searching algorithms, and genetic algorithms, can be applied in AI. Heuristic algorithms allow for experimentation and testing, offering effective short-term methods for achieving goals. Searching algorithms explore a search space to find the most appropriate solutions, while genetic algorithms generate and evolve potential solutions based on fitness criteria.

In conclusion, problem formulation and problem solving in AI play a vital role in shaping the field of artificial intelligence. By applying these techniques and approaches, intelligent systems and applications can be developed to tackle complex challenges and improve our lives.

What is problem formulation in artificial intelligence?

Problem formulation in artificial intelligence is the process of identifying, analyzing, and defining the problems that need to be solved using AI techniques. It involves breaking down complex problems into smaller components and formulating a clear problem statement.

What are the three types of problems in AI?

The three types of problems in AI are ignorable, recoverable, and irrecoverable. Ignorable problems are those where certain solution steps can be ignored without affecting the final outcome. Recoverable problems are those where solution steps can be undone if needed. Irrecoverable problems are those where solution steps cannot be undone.

What are the steps for problem solving in AI?

The steps for problem solving in AI include problem definition, problem analysis, knowledge representation, problem-solving, and formulation of associated problem components. Problem definition involves specifying the inputs and acceptable system solutions, while problem analysis entails analyzing the problem thoroughly. Knowledge representation involves collecting detailed information about the problem, and problem-solving is the selection of the best techniques to solve the problem. Formulating the associated problem components includes defining the initial state, actions, transitions, goal test, and path costing.

What are the problem-solving approaches in AI?

The problem-solving approaches in AI include heuristic algorithms, searching algorithms, and genetic algorithms. Heuristic algorithms experiment and test procedures to generate a solution, searching algorithms involve creating and exploring a search space, and genetic algorithms generate and evolve populations of potential solutions based on fitness criteria.

What is the role of problem formulation and problem solving in AI?

Problem formulation and problem solving in AI play a vital role in shaping efficient and smart solutions. Problem formulation helps AI agents define the goals, initial state, actions, transitions, and goal test required to solve a problem, while problem solving techniques and approaches enable the development of intelligent systems and applications in the field of artificial intelligence.

Source Links

• https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future
• https://www.geeksforgeeks.org/problem-solving-in-artificial-intelligence/
• https://www.javatpoint.com/problem-solving-techniques-in-ai

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OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step

OpenAI made the last big breakthrough in artificial intelligence by increasing the size of its models to dizzying proportions, when it introduced GPT-4 last year. The company today announced a new advance that signals a shift in approach—a model that can “reason” logically through many difficult problems and is significantly smarter than existing AI without a major scale-up.

The new model, dubbed OpenAI o1, can solve problems that stump existing AI models, including OpenAI’s most powerful existing model, GPT-4o . Rather than summon up an answer in one step, as a large language model normally does, it reasons through the problem, effectively thinking out loud as a person might, before arriving at the right result.

“This is what we consider the new paradigm in these models,” Mira Murati , OpenAI’s chief technology officer, tells WIRED. “It is much better at tackling very complex reasoning tasks.”

The new model was code-named Strawberry within OpenAI, and it is not a successor to GPT-4o but rather a complement to it, the company says.

Murati says that OpenAI is currently building its next master model, GPT-5, which will be considerably larger than its predecessor. But while the company still believes that scale will help wring new abilities out of AI, GPT-5 is likely to also include the reasoning technology introduced today. “There are two paradigms,” Murati says. “The scaling paradigm and this new paradigm. We expect that we will bring them together.”

LLMs typically conjure their answers from huge neural networks fed vast quantities of training data. They can exhibit remarkable linguistic and logical abilities, but traditionally struggle with surprisingly simple problems such as rudimentary math questions that involve reasoning.

Murati says OpenAI o1 uses reinforcement learning, which involves giving a model positive feedback when it gets answers right and negative feedback when it does not, in order to improve its reasoning process. “The model sharpens its thinking and fine tunes the strategies that it uses to get to the answer,” she says. Reinforcement learning has enabled computers to play games with superhuman skill and do useful tasks like designing computer chips . The technique is also a key ingredient for turning an LLM into a useful and well-behaved chatbot.

Mark Chen, vice president of research at OpenAI, demonstrated the new model to WIRED, using it to solve several problems that its prior model, GPT-4o, cannot. These included an advanced chemistry question and the following mind-bending mathematical puzzle: “A princess is as old as the prince will be when the princess is twice as old as the prince was when the princess’s age was half the sum of their present age. What is the age of the prince and princess?” (The correct answer is that the prince is 30, and the princess is 40).

“The [new] model is learning to think for itself, rather than kind of trying to imitate the way humans would think,” as a conventional LLM does, Chen says.

OpenAI says its new model performs markedly better on a number of problem sets, including ones focused on coding, math, physics, biology, and chemistry. On the American Invitational Mathematics Examination (AIME), a test for math students, GPT-4o solved on average 12 percent of the problems while o1 got 83 percent right, according to the company.

The new model is slower than GPT-4o, and OpenAI says it does not always perform better—in part because, unlike GPT-4o, it cannot search the web and it is not multimodal, meaning it cannot parse images or audio.

Improving the reasoning capabilities of LLMs has been a hot topic in research circles for some time. Indeed, rivals are pursuing similar research lines. In July, Google announced AlphaProof , a project that combines language models with reinforcement learning for solving difficult math problems.

AlphaProof was able to learn how to reason over math problems by looking at correct answers. A key challenge with broadening this kind of learning is that there are not correct answers for everything a model might encounter. Chen says OpenAI has succeeded in building a reasoning system that is much more general. “I do think we have made some breakthroughs there; I think it is part of our edge,” Chen says. “It’s actually fairly good at reasoning across all domains.”

Noah Goodman , a professor at Stanford who has published work on improving the reasoning abilities of LLMs, says the key to more generalized training may involve using a “carefully prompted language model and handcrafted data” for training. He adds that being able to consistently trade the speed of results for greater accuracy would be a “nice advance.”

Yoon Kim , an assistant professor at MIT, says how LLMs solve problems currently remains somewhat mysterious, and even if they perform step-by-step reasoning there may be key differences from human intelligence. This could be crucial as the technology becomes more widely used. “These are systems that would be potentially making decisions that affect many, many people,” he says. “The larger question is, do we need to be confident about how a computational model is arriving at the decisions?”

The technique introduced by OpenAI today also may help ensure that AI models behave well. Murati says the new model has shown itself to be better at avoiding producing unpleasant or potentially harmful output by reasoning about the outcome of its actions. “If you think about teaching children, they learn much better to align to certain norms, behaviors, and values once they can reason about why they’re doing a certain thing,” she says.

Oren Etzioni , a professor emeritus at the University of Washington and a prominent AI expert, says it’s “essential to enable LLMs to engage in multi-step problem solving, use tools, and solve complex problems.” He adds, “Pure scale up will not deliver this.” Etzioni says, however, that there are further challenges ahead. “Even if reasoning were solved, we would still have the challenge of hallucination and factuality.”

OpenAI’s Chen says that the new reasoning approach developed by the company shows that advancing AI need not cost ungodly amounts of compute power. “One of the exciting things about the paradigm is we believe that it’ll allow us to ship intelligence cheaper,” he says, “and I think that really is the core mission of our company.”

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The Intersection of Math and AI: A New Era in Problem-Solving

Conference is exploring burgeoning connections between the two fields.

Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.

Embracing Machine Learning in Mathematics

“Mathematicians are beginning to embrace machine learning,” says Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics at Caltech, who put together the Mathematics and Machine Learning 2023 conference, which is taking place at Caltech December 10–13.

“There are some mathematicians who may still be skeptical about using the tools,” Gukov says. “The tools are mischievous and not as pure as using paper and pencil, but they work.”

Machine Learning: A New Era in Mathematical Problem Solving

Machine learning is a subfield of AI, or artificial intelligence , in which a computer program is trained on large datasets and learns to find new patterns and make predictions. The conference, the first put on by the new Richard N. Merkin Center for Pure and Applied Mathematics, will help bridge the gap between developers of machine learning tools (the data scientists) and the mathematicians. The goal is to discuss ways in which the two fields can complement each other.

Mathematics and Machine Learning: A Two-Way Street

“It’s a two-way street,” says Gukov, who is the director of the new Merkin Center, which was established by Caltech Trustee Richard Merkin.

“Mathematicians can help come up with clever new algorithms for machine learning tools like the ones used in generative AI programs like ChatGPT, while machine learning can help us crack difficult math problems.”

Yi Ni, a professor of mathematics at Caltech, plans to attend the conference, though he says he does not use machine learning in his own research, which involves the field of topology and, specifically, the study of mathematical knots in lower dimensions. “Some mathematicians are more familiar with these advanced tools than others,” Ni says. “You need to know somebody who is an expert in machine learning and willing to help. Ultimately, I think AI for math will become a subfield of math.”

The Riemann Hypothesis and Machine Learning

One tough problem that may unravel with the help of machine learning, according to Gukov, is known as the Riemann hypothesis. Named after the 19th-century mathematician Bernhard Riemann, this problem is one of seven Millennium Problems selected by the Clay Mathematics Institute; a $1 million prize will be awarded for the solution to each problem.

The Riemann hypothesis centers around a formula known as the Riemann zeta function, which packages information about prime numbers. If proved true, the hypothesis would provide a new understanding of how prime numbers are distributed. Machine learning tools could help crack the problem by providing a new way to run through more possible iterations of the problem.

Mathematicians and Machine Learning: A Synergistic Relationship

“Machine learning tools are very good at recognizing patterns and analyzing very complex problems,” Gukov says.

Ni agrees that machine learning can serve as a helpful assistant. “Machine learning solutions may not be as beautiful, but they can find new connections,” he says. “But you still need a mathematician to turn the questions into something computers can solve.”

Knot Theory and Machine Learning

Gukov has used machine learning himself to untangle problems in knot theory. Knot theory is the study of abstract knots, which are similar to the knots you might find on a shoestring, but the ends of the strings are closed into loops. These mathematical knots can be entwined in various ways, and mathematicians like Gukov want to understand their structures and how they relate to each other. The work has relationships to other fields of mathematics such as representation theory and quantum algebra, and even quantum physics.

In particular, Gukov and his colleagues are working to solve what is called the smooth Poincaré conjecture in four dimensions. The original Poincaré conjecture, which is also a Millennium Problem, was proposed by mathematician Henri Poincaré early in the 20th century. It was ultimately solved from 2002 to 2003 by Grigori Perelman (who famously turned down his prize of $1 million). The problem involves comparing spheres to certain types of manifolds that look like spheres; manifolds are shapes that are projections of higher-dimensional objects onto lower dimensions. Gukov says the problem is like asking, “Are objects that look like spheres really spheres?”

The four-dimensional smooth Poincaré conjecture holds that, in four dimensions, all manifolds that look like spheres are indeed actually spheres. In an attempt to solve this conjecture, Gukov and his team develop a machine learning approach to evaluate so-called ribbon knots.

“Our brain cannot handle four dimensions, so we package shapes into knots,” Gukov says. “A ribbon is where the string in a knot pierces through a different part of the string in three dimensions but doesn’t pierce through anything in four dimensions. Machine learning lets us analyze the ‘ribboness’ of knots, a yes-or-no property of knots that has applications to the smooth Poincaré conjecture.”

“This is where machine learning comes to the rescue,” writes Gukov and his team in a preprint paper titled “ Searching for Ribbons with Machine Learning .” “It has the ability to quickly search through many potential solutions and, more importantly, to improve the search based on the successful ‘games’ it plays. We use the word ‘games’ since the same types of algorithms and architectures can be employed to play complex board games, such as Go or chess, where the goals and winning strategies are similar to those in math problems.”

The Interplay of Mathematics and Machine Learning Algorithms

On the flip side, math can help in developing machine learning algorithms, Gukov explains. A mathematical mindset, he says, can bring fresh ideas to the development of the algorithms behind AI tools. He cites Peter Shor as an example of a mathematician who brought insight to computer science problems. Shor, who graduated from Caltech with a bachelor’s degree in mathematics in 1981, famously came up with what is known as Shor’s algorithm, a set of rules that could allow quantum computers of the future to factor integers faster than typical computers, thereby breaking digital encryption codes.

Today’s machine learning algorithms are trained on large sets of data. They churn through mountains of data on language, images, and more to recognize patterns and come up with new connections. However, data scientists don’t always know how the programs reach their conclusions. The inner workings are hidden in a so-called “black box.” A mathematical approach to developing the algorithms would reveal what’s happening “under the hood,” as Gukov says, leading to a deeper understanding of how the algorithms work and thus can be improved.

“Math,” says Gukov, “is fertile ground for new ideas.”

The conference will take place at the Merkin Center on the eighth floor of Caltech Hall.

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October 29, 2021

Solving math word problems

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.

Why it matters

This is important because today’s AI is still quite weak at commonsense multistep reasoning, which is easy even for grade school kids. We achieved these results by training our model to recognize its mistakes, so that it can try repeatedly until it finds a solution that works.

Introduction

Large language models like GPT-3 have many impressive skills, including their ability to imitate many writing styles, and their extensive factual knowledge. However, they struggle to perform tasks that require accurate multistep reasoning, like solving grade school math word problems. Although the model can mimic the cadence of correct solutions, it regularly produces critical errors in logic.

To match human performance in complex logical domains, our models must learn to recognize their mistakes and to choose their steps carefully. To that end, we train verifiers to evaluate whether or not a proposed solution is correct. To solve a new problem, we use verifiers to select the best among many proposed solutions. We collected the new GSM8K dataset to evaluate our methods, and we are releasing this dataset to facilitate research.

In the ten examples below, we show solutions generated by our new method, verification, and our baseline method, fine-tuning.

GSM8K dataset

GSM8K consists of 8.5K high quality grade school math word problems. Each problem takes between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − × ÷) to reach the final answer. Fine-tuned state-of-the-art language models perform poorly on this dataset, primarily due to the high diversity of problems. At the same time, GSM8K solutions depend only on elementary concepts, so achieving high test performance is a tractable goal.

Solutions in GSM8K are written as natural language rather than as pure math expressions. By sticking to natural language, model-generated solutions are more readily interpretable by humans, and our methods remain relatively domain agnostic.

Training verifiers: Models that learn from their mistakes

One significant challenge in mathematical reasoning is the high sensitivity to individual mistakes. Autoregressive models, which generate each solution token by token, have no mechanism to correct their own errors. Solutions that veer off-course quickly become unrecoverable, as can be seen in the examples provided.

We address this problem by training verifiers to evaluate the correctness of model-generated solutions. Verifiers are given many possible solutions, all written by the model itself, and they are trained to decide which ones, if any, are correct.

To solve a new problem at test time, we generate 100 candidate solutions and then select the solution that is ranked highest by the verifier. Verifiers benefit from this inherent optionality, as well as from the fact that verification is often a simpler task than generation.

We find that we get a strong boost in performance from verification, as long as the dataset is large enough. With datasets that are too small, we believe that the verifiers overfit by memorizing the final answers in the training set, rather than learning any more useful properties of mathematical reasoning.

On the full training set, 6B parameter verification slightly outperforms a fine-tuned 175B parameter model, giving a performance boost that is approximately equivalent to a 30x model size increase. Moreover, verification appears to scale more effectively with additional data, if we extrapolate based on current results.

Producing correct arguments and recognizing incorrect ones are key challenges in developing more general AI. Grade school math is an ideal testbed for these capabilities. The problems in GSM8K are conceptually simple, yet one subtle mistake is enough to derail an entire solution. Identifying and avoiding such mistakes is a crucial skill for our models to develop. By training verifiers, we teach our models to separate the good solutions from the ones that didn’t quite work out. We expect these skills to become increasingly relevant as we attempt to apply our models to more logically complex domains.

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Acknowledgments

Thanks to the team at Surge AI for performing the GSM8K data collection.

Thanks to our paper co-authors: Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and Christopher Hesse.

Thanks to those who provided feedback on this release: Dan Hendrycks, Leo Gao, Alec Radford, Giambattista Parascandolo, Harri Edwards, Yura Burda, Nick Ryder, Ilya Sutskever, Mira Murati, Sam Altman, Aris Konstantinidis, Andrew Mayne, Hannah Wong, and Steve Dowling.

Thank you to the students who volunteered to take our test!

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Characteristics of Artificial Intelligence Problems

Problems in Artificial Intelligence (AI) come in different forms, each with its own set of challenges and potential for innovation. From image recognition to natural language processing, AI problems exhibit distinct characteristics that shape the strategies and techniques used to tackle them effectively. In this article, we delve into the fundamental characteristics of AI problems, providing light on what makes them so fascinating and formidable.

Table of Content

Key Terminologies in Artificial Intelligence Problems

Addressing the challenges of ai problems, examples of ai applications and challenges across domains, characteristics of artificial intelligence problems – faqs.

Before exploring the characteristics, let’s clarify some essential AI concepts:

• Problem-solving: Problem-solving is a process that is a solution provided to a complex problem or task. When dealing with AI, problem-solving involves creating algorithms and methods of artificial intelligence that will empower machines to imitate humans’ capabilities of logical and reasonable thinking in certain situations.
• Search Space: Searching space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution. It covers a gamut of options that the agent might select for arriving at the same destination.
• State: An entity represents some unique and specific arrangement of elements in a problem-solving situation. States can be assigned to different locations, challenges, or dangers that the problem-solving agent faces while looking for a solution to the problem within the search space.
• Search Algorithm: A search algorithm describes any process or method targeted for examining and exploring the given problem space to find a solution. Algorithm decision-making has diverging levels of complexity and effectiveness. They are studied to help in the discovery of the most suitable results.
• Heuristic: Heuristic is a thumb rule or guiding principle that is used to make intelligent decisions or solve the problems that are encountered during the process. Applying heuristics in AI is prevalent in prioritizing search paths or evaluating probable solutions based on their likelihood of finishing successfully.
• Optimization: The problem of optimization implies finding the best solution for process selection among the set of feasible alternatives submitted to some previously set objectives or criteria. AI optimization approaches are employed to deal optimally with complex issues through performance and efficiency improvement.

By understanding these key terminologies, we can better grasp the characteristics of AI problems and the techniques used to address them. These concepts form the foundation of AI problem-solving and provide the framework for developing innovative solutions to real-world challenges.

Let’s explore the core characteristics that differentiate AI problems:

• Learning and adaptation: AI systems should be capable of learning from data or experiences and adapting their behaviour accordingly. This enables them to improve performance over time and handle new situations more effectively.
• Complexity: AI problems often involve dealing with complex systems or large amounts of data. AI systems must be able to handle this complexity efficiently to produce meaningful results.
• Uncertainty: AI systems frequently operate in environments where outcomes are uncertain or incomplete information is available. They must be equipped to make decisions or predictions under such conditions.
• Dynamism: Environments in which AI systems operate can change over time. These changes may occur unpredictably or according to specific rules, requiring AI systems to continually adjust their strategies or models.
• Interactivity : Many AI applications involve interaction with users or other agents. Effective AI systems should be able to perceive, interpret, and respond to these interactions in a meaningful way.
• Context dependence: The behavior or performance of AI systems may depend on the context in which they operate. Understanding and appropriately responding to different contexts is essential for achieving desired outcomes.
• Multi-disciplinary: AI problems often require knowledge and techniques from multiple disciplines, including computer science, mathematics, statistics, psychology, and more. Integrating insights from these diverse fields is necessary for developing effective AI solutions.
• Goal-oriented Design: AI systems are typically designed to achieve specific objectives or goals. Designing AI systems with clear objectives in mind helps guide the development process and ensures that the resulting systems are focused on achieving meaningful outcomes.

These characteristics collectively shape the challenges and opportunities involved in developing and deploying AI systems across various domains and applications.

The characteristics of AI problems present unique challenges that require innovative approaches to solution development. Some of the key aspects to consider in tackling these challenges include:

• Complexity and Uncertainty: AI difficulties are sometimes characterized by highly variable domains that are difficult to predict exactly. Hence, AI algorithms should be installed with the skill of dealing with unclear circumstances and should make decisions that are based on imperfect data or noisy information.
• Algorithmic Efficiency: Among the key challenges of this approach are the enormous search spaces, computational resources, and the efficiency of the algorithms in terms of problem-solving. Strategies like caching, pruning, and parallelization are among the most widely used implementations for better algorithmic speed.
• Domain Knowledge Integration: Such numerous AI problems involve the ability to capture the rules and reasoning of the real world to model and solve the questions correctly. The AI machines that have been trained with expertise from relevant domains improve the accuracy and effectiveness of the applications in the real world.
• Scalability and Adaptability: AI solutions should be able to process large datasets and complex cases at the same time, and they should also be versatile by responding to shifts in conditions and requirements. Strategies such as machine learning and reinforcement learning allow systems to do more than just perform according to the given tasks at hand; they empower systems to learn and progress over time.
• Ethical and Social Implications: AI technologies elicit ethical and social limitations concerning problems of bias, justice, privacy, and responsible office. Taking these implications into account, along with ethical frameworks, compliance frameworks, and stakeholder engagement, is essential. This approach will help position cryptocurrencies as a secure and trustworthy investment.
• Interpretability and Explainability: To achieve interpretability and explainability of AI algorithms for the sake of understanding and confidence among users and stakeholders, these algorithms should be knowable and comprehensible enough. Examples like chatbots producing natural-like conversation could better clarify the working scheme of AI technology.
• Robustness and Resilience: AI machinery should perform against its being hacked or affected by adversarial attacks, inaccuracies (errors), and environmental changes. Robustness testing, the construction of mechanisms for error handling, and the building up of redundancy must be taken seriously by AI systems to ensure their reliability and stability.
• Human-AI Collaboration: Successful human-AI entente is the key component to making the most of our advantages as well as artificial intelligence skills. Achieving AI solutions that are capable of supporting human skills and more importantly, preferences will reduce human efforts correspondingly and bring the best performance.

By addressing these challenges through innovative methodologies and interdisciplinary collaboration, we can harness the full potential of AI to solve complex problems and drive societal progress.

1. Robotics

Problem: A delivery robot navigating a busy warehouse to locate and retrieve a specific item.

Characteristics:

• Complexity: Industrial storage is networked, in the middle of things, with obstacles, and other robots and people moving unpredictably. This robot must process the visual scene, plan the route effectively, and detect and avoid possible collisions.
• Dynamism: A combination of outside factors leads to change, which is a constant inside the warehouse. Unpredictable system failures or spontaneous tasks can make the robot change its means and decision-making at the moment of need.
• Uncertainty: Sensor data (such as images obtained from a camera) might be noisy, incomplete, and unstable. The robot could be handling decisions based on fragmented or formless pieces of information.

2. Natural Language Processing (NLP)

Problem: A sentiment analysis system in NLP classifying customer reviews as positive, negative, or neutral.

• Subjectivity: Human language is nuanced. Sarcasm, irony, and figurative expressions can be difficult for machines to accurately interpret.
• Need for Context: Understanding sentiment may depend on cultural references, product-specific knowledge, or even the reviewer’s prior interactions with the company.
• Ambiguity: A single word or phrase could have multiple meanings, affecting the overall sentiment of the text.

3. Computer Vision

Problem: A medical image recognition system in Computer Vision designed to detect tumors in X-rays or MRI scans.

• Complexity: Medical images are highly detailed and can exhibit subtle variations. The system needs to distinguish between healthy tissue and potential abnormalities.
• Uncertainty: Images may contain noise or artifacts. The presence of a tumor might not be immediately obvious, requiring the system to handle ambiguity.
• Ethical Considerations: False positives or false negatives have serious consequences for patient health. Accuracy, transparency, and minimizing bias are crucial.

The premises of AI-based problems – complexity, uncertainty, subjectivity, and more, – bring an unavoidable difficulty to the table. These features must be known for building appropriate AI because this is necessary. Through the use of machine learning, probabilistic reasoning, and knowledge representation which are referred to as the tools in AI development alongside the ethical considerations, these designers and scientists can face such complexities well and give shape to AI in a way that will be beneficial to society.

Q. What are the core characteristics that differentiate AI problems?

The core characteristics of AI problems include complexity, uncertainty and ambiguity, lack of clear problem definition, non-linearity, dynamism, subjectivity, interactivity, context sensitivity, and ethical considerations.

Q. Can you explain the concept of problem-solving in AI?

Problem-solving in AI involves creating algorithms and methods that enable machines to imitate human capabilities of logical and reasonable thinking in certain situations.

Q. What is meant by the term “search space” in AI?

Search space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution.

Q. How do AI algorithms address challenges such as complexity and uncertainty?

AI algorithms are designed to handle unclear circumstances and make decisions based on imperfect data or noisy information.

Q. What are some examples of AI applications and the challenges they face?

Examples include robotics (e.g., delivery robots navigating busy warehouses), natural language processing (e.g., sentiment analysis of customer reviews), and computer vision (e.g., medical image recognition for detecting tumors).

Q. What role do ethical considerations play in AI development?

Ethical considerations are crucial in AI development to address issues such as bias, justice, privacy, and responsibility, ensuring that AI technologies are deployed responsibly and ethically.

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To reason is to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive . An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore, he is in the museum,” and of the latter is, “Previous accidents of this sort were caused by instrument failure. This accident is of the same sort; therefore, it was likely caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case, the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case, the truth of the premises lends support to the conclusion without giving absolute assurance . Inductive reasoning is common in science , where data are collected and tentative models are developed to describe and predict future behavior—until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic , where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.

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There has been considerable success in programming computers to draw inferences. However, true reasoning involves more than just drawing inferences: it involves drawing inferences relevant to the solution of the particular problem. This is one of the hardest problems confronting AI.

Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental , reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot , this might consist of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT, and MOVERIGHT—until the goal is reached.

Many diverse problems have been solved by artificial intelligence programs. Some examples are finding the winning move (or sequence of moves) in a board game, devising mathematical proofs, and manipulating “virtual objects” in a computer-generated world.

In perception the environment is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field. At present, artificial perception is sufficiently advanced to enable optical sensors to identify individuals and enable autonomous vehicles to drive at moderate speeds on the open road.

A language is a system of signs having meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a mini-language, it being a matter of convention that ⚠ means “hazard ahead” in some countries. It is distinctive of languages that linguistic units possess meaning by convention, and linguistic meaning is very different from what is called natural meaning, exemplified in statements such as “Those clouds mean rain” and “The fall in pressure means the valve is malfunctioning.”

An important characteristic of full-fledged human languages—in contrast to birdcalls and traffic signs—is their productivity. A productive language can formulate an unlimited variety of sentences.

Large language models like ChatGPT can respond fluently in a human language to questions and statements. Although such models do not actually understand language as humans do but merely select words that are more probable than others, they have reached the point where their command of a language is indistinguishable from that of a normal human. What, then, is involved in genuine understanding, if even a computer that uses language like a native human speaker is not acknowledged to understand? There is no universally agreed upon answer to this difficult question.

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AI accelerates problem-solving in complex scenarios

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.

While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Siriu Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems.

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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AI accelerates problem-solving in complex scenarios

While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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When Should You Use AI to Solve Problems?

Not every challenge requires an algorithmic approach.

AI is increasingly informing business decisions but can be misused if executives stick with old decision-making styles. A key to effective collaboration is to recognize which parts of a problem to hand off to the AI and which the managerial mind will be better at solving. While AI is superior at data-intensive prediction problems, humans are uniquely suited to the creative thought experiments that underpin the best decisions.

Business leaders often pride themselves on their intuitive decision-making. They didn’t get to be division heads and CEOs by robotically following some leadership checklist. Of course, intuition and instinct can be important leadership tools, but not if they’re indiscriminately applied.

• Bob Suh is the founder and CEO of OnCorps, which provides AI-based decision guidance systems to the financial services industry. The firm works with leading scientists at Yale, Oxford, and Harvard to test decision making and behavioral algorithms. Previously, he was the chief technology strategist at Accenture.

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Can AI help you solve problems?

May 21, 2023 AI technology has revolutionized the way organizations do business; now, with proper guardrails in place, generative AI promises to not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones. “Companies across sectors, from pharmaceuticals to banking to retail, are already standing up a range of use cases to capture value creation potential,” write Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya  in a new article . Generative AI is nascent, but as it develops and becomes increasingly, and more seamlessly, incorporated into business, its problem-solving potential will intensify. Check out these insights to understand how both AI and generative AI can help your organization solve complex problems, transform operations, improve products, and realize new revenue streams.

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How leaders are using ai as a problem-solving tool.

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Leaders face more complex decisions than ever before. For example, many must deliver new and better services for their communities while meeting sustainability and equity goals. At the same time, many need to find ways to operate and manage their budgets more efficiently. So how can these leaders make complex decisions and get them right in an increasingly tricky business landscape? The answer lies in harnessing technological tools like Artificial Intelligence (AI).

CHONGQING, CHINA - AUGUST 22: A visitor interacts with a NewGo AI robot during the Smart China Expo ... [+] 2022 on August 22, 2022 in Chongqing, China. The expo, held annually in Chongqing since 2018, is a platform to promote global exchanges of smart technologies and international cooperation in the smart industry. (Photo by Chen Chao/China News Service via Getty Images)

What is AI?

AI can help leaders in several different ways. It can be used to process and make decisions on large amounts of data more quickly and accurately. AI can also help identify patterns and trends that would otherwise be undetectable. This information can then be used to inform strategic decision-making, which is why AI is becoming an increasingly important tool for businesses and governments. A recent study by PwC found that 52% of companies accelerated their AI adoption plans in the last year. In addition, 86% of companies believe that AI will become a mainstream technology at their company imminently. As AI becomes more central in the business world, leaders need to understand how this technology works and how they can best integrate it into their operations.

At its simplest, AI is a computer system that can learn and work independently without human intervention. This ability makes AI a powerful tool. With AI, businesses and public agencies can automate tasks, get insights from data, and make decisions with little or no human input. Consequently, AI can be a valuable problem-solving tool for leaders across the private and public sectors, primarily through three methods.

1) Automation

One of AI’s most beneficial ways to help leaders is by automating tasks. This can free up time to focus on other essential things. For example, AI can help a city save valuable human resources by automating parking enforcement. In addition, this will help improve the accuracy of detecting violations and prevent costly mistakes. Automation can also help with things like appointment scheduling and fraud detection.

2) Insights from data

Another way AI can help leaders solve problems is by providing insights from data. With AI, businesses can gather large amounts of data and then use that data to make better decisions. For example, suppose a company is trying to decide which products to sell. In that case, AI can be used to gather data about customer buying habits and then use that data to make recommendations about which products to market.

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3) Simulations

Finally, AI can help leaders solve problems by allowing them to create simulations. With AI, organizations can test out different decision scenarios and see what the potential outcomes could be. This can help leaders make better decisions by examining the consequences of their choices. For example, a city might use AI to simulate different traffic patterns to see how a new road layout would impact congestion.

Choosing the Right Tools

Artificial intelligence and machine learning technologies can revolutionize how governments and businesses solve real-world problems,” said Chris Carson, CEO of Hayden AI, a global leader in intelligent enforcement technologies powered by artificial intelligence. His company addresses a problem once thought unsolvable in the transit world: managing illegal parking in bus lanes in a cost effective, scalable way.

Illegal parking in bus lanes is a major problem for cities and their transit agencies. Cars and trucks illegally parked in bus lanes force buses to merge into general traffic lanes, significantly slowing down transit service and making riders’ trips longer. That’s where a company like Hayden AI comes in. “Hayden AI uses artificial intelligence and machine learning algorithms to detect and process illegal parking in bus lanes in real-time so that cities can take proactive measures to address the problem ,” Carson observes.

Illegal parking in bus lanes is a huge problem for transit agencies. Hayden AI works with transit ... [+] agencies to fix this problem by installing its AI-powered camera systems on buses to conduct automated enforcement of parking violations in bus lanes

In this case, an AI-powered camera system is installed on each bus. The camera system uses computer vision to “watch” the street for illegal parking in the bus lane. When it detects a traffic violation, it sends the data back to the parking authority. This allows the parking authority to take action, such as sending a ticket to the offending vehicle’s owner.

The effectiveness of AI is entirely dependent on how you use it. As former Accenture chief technology strategist Bob Suh notes in the Harvard Business Review, problem-solving is best when combined with AI and human ingenuity. “In other words, it’s not about the technology itself; it’s about how you use the technology that matters. AI is not a panacea for all ills. Still, when incorporated into a company’s problem-solving repertoire, it can be an enormously powerful tool,” concludes Terence Mauri, founder of Hack Future Lab, a global think tank.

Split the Responsibility

Huda Khan, an academic researcher from the University of Aberdeen, believes that AI is critical for international companies’ success, especially in the era of disruption. Khan is calling international marketing academics’ research attention towards exploring such transformative approaches in terms of how these inform competitive business practices, as are international marketing academics Michael Christofi from the Cyprus University of Technology; Richard Lee from the University of South Australia; Viswanathan Kumar from St. John University; and Kelly Hewett from the University of Tennessee. “AI is very good at automating repetitive tasks, such as customer service or data entry. But it’s not so good at creative tasks, such as developing new products,” Khan says. “So, businesses need to think about what tasks they want to automate and what tasks they want to keep for humans.”

Khan believes that businesses need to split the responsibility between AI and humans. For example, Hayden AI’s system is highly accurate and only sends evidence packages of potential violations for human review. Once the data is sent, human analysis is still needed to make the final decision. But with much less work to do, government agencies can devote their employees to tasks that can’t be automated.

Backed up by efficient, effective data analysis, human problem-solving can be more innovative than ever. Like all business transitions, developing the best system for combining human and AI work might take some experimentation, but it can significantly impact future success. For example, if a company is trying to improve its customer service, it can use AI startup Satisfi’s natural language processing technology . This technology can understand a customer’s question and find the best answer from a company’s knowledge base. Likewise, if a company tries to increase sales, it can use AI startup Persado’s marketing language generation technology . This technology can be used to create more effective marketing campaigns by understanding what motivates customers and then generating language that is more likely to persuade them to make a purchase.

Look at the Big Picture

A technological solution can frequently improve performance in multiple areas simultaneously. For instance, Hayden AI’s automated enforcement system doesn’t just help speed up transit by keeping bus lanes clear for buses; it also increases data security by limiting how much data is kept for parking enforcement, which allows a city to increase the efficiency of its transportation while also protecting civil liberties.

This is the case with many technological solutions. For example, an e-commerce business might adopt a better data architecture to power a personalized recommendation option and benefit from improved SEO. As a leader, you can use your big-picture view of your company to identify critical secondary benefits of technologies. Once you have the technologies in use, you can also fine-tune your system to target your most important priorities at once.

In summary, AI technology is constantly evolving, becoming more accessible and affordable for businesses of all sizes. By harnessing the power of AI, leaders can make better decisions, improve efficiency, and drive innovation. However, it’s important to remember that AI is not a silver bullet. Therefore, organizations must use AI and humans to get the best results.

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“Our purpose is solving customer problems”: OSS President Rahul Atri on Inside Track

Rakuten Symphony is a standout player in telecom because of its unique ability to understand and solve customer problems, the President of its OSS Business Unit (OSS BU) has said.  

In the second edition of ‘Inside Track’, the new employee-facing live series hosted by comms director James Dartnell, Rahul Atri outlined the importance of understanding customer needs, why Rakuten has a strong track record of cultivating leaders and why “AI-washing” will not deliver value to mobile network operators.  

Atri returned to Rakuten Group as Rakuten Symphony OSS BU President in 2023 having previously played a central role in deploying Rakuten Mobile's network - one based on an autonomous, cloud-native, end-to-end network architecture. He now leads a division of over 1,000 employees – Rakuten Symphony’s largest BU, the majority of which comprises engineers.

The Inside Track session kicked off with Atri emphasising the telecom industry’s fascination with the Rakuten telecom story. Rakuten Symphony’s experience in operating Rakuten Mobile’s network gives the organisation an ability to put “ourselves in the customer's shoes and help solve their problems with technology,” he said.

“They want to learn from us in terms of how we created things and how we solve problems. What customers love about us is we are not just there to sell more boxes and software. We own their problem and solve that end-to-end. We've been successful at doing that.

“I've been in multiple conversations with customers where they're not initially able to articulate some of their challenges, but we’re able to tell them a story that shows we’ve gone through the same journey and how we overcame that challenge. We have a product platform which is run across the lifecycle of Rakuten Mobile. I think that's our differentiator - that’s extremely helpful to customers.”  

Atri highlighted how Rakuten Symphony’s unique approach to solving these problems has supported one of the world’s largest brownfield operators in retiring 15 legacy applications, with more to come. “That’s big – they’ve got so many technologies as part of their operation,” he said. If you want to work with brownfield operators and solve their problems, you need to have a culture in which you continuously innovate.”  

The second episode of Inside Track hosted two audience participation polls, the second of which revealed an eye-opening result - 70% of audience members agreed that the most important factor in building innovative and effective products and solutions is through a culture that enables employees to be creative - and to fail – a sentiment with which Atri strongly agreed.  

“I want alchemists in my team - people who can connect the dots from what the customer is looking for to what the product should be. I think it's also very important in today's world to be a storyteller if you want to climb up the ladder faster. My advice is to be a ‘kid’ - be curious about everything. Don’t be scared of being judged. The more you experience you gain - especially in telecom – the more you build walls and a safety net around you. Learning never stops.”

Team work makes the dream work

Atri went on to highlight the importance of successfully unifying experts across UX, product management and quality assurance teams to deliver products and experiences that deliver value. “Whoever is working on products, whether it is the designer who creates the beautiful layouts and UX, the quality assurance team who really stress test the product, and developers who write the code – they all need to understand what the essence of the product is for the customer, how they're going to use it. In the OSS BU we know whatever code each employee is writing and where that is being used, how it looks, what customer uses it, and where it can be improved.”

No ‘AI-washing’

Understanding user intent is fundamental to Rakuten’s approach to developing AI services that can make a tangible difference to mobile network operators, Atri said. “We focus a lot on the ROI of our products - we don't want to just wash them with AI, to say we have a chatbot enabled. What customers really need is more insight and value. Today, anyone can talk to an AI platform. What we are working on is how to convert this into a mechanism where the tool can take care of the rest. We want to become a platform which can make a network programmable - where you can convey your intent to the platform, and the platform listens to you and takes care of the rest, whether it is scaling out a new application, deploying an edge site, reconfiguring itself or deploying networks to support slicing. The platform needs to understand whoever is behind the screen – whether it is a NOC engineer, a salesperson or the CEO - and what they are specifically asking.”

The conversation concluded with Atri sharing his take on Rakuten’s strong track record of developing leaders internally – a culture that he himself has directly experienced. “Rakuten is the place where curiosity gets its wings,” he said. “Rakuten Symphony has been the disruptor. We've been the challenger. I think that comes from the leadership and the DNA we carry. We are a big company, but we have the heart and soul of a startup.”  

Atri is now striving to continue this positive example by increasing the number of female leaders within the OSS BU. He gave a nod to Subha Srinivasan, Rakuten Symphony’s Global Head of Customer Excellence, as an example to other employees. “I’m trying to increase the number of female mentors in our organization, and Subha is a great example of that. What we have seen is that female leaders are often faster, more agile, more complete.”

“Read every day”

Atri’s Inside Track episode garnered extensive audience interest, and he rounded off the discussion by addressing an audience question – how can tech specialists transition to becoming business leaders without a business background, and how should they start that journey? “ Read every day,” Atri said. “Then spend time going out of your comfort zone. When you don't know something, be bullish and ask as many questions as you need to. You need to be boundary-less, own problems and find a way to solve them, despite whatever challenges are in the way.”

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