main function of problem solving agent is to

Artificial Intelligence Questions and Answers – Problem Solving

This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Problem Solving”.

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What is the problem-solving agent in artificial intelligence?

Are you curious to know how machines can solve complex problems, just like humans? Enter the world of artificial intelligence and meet one of its most critical players- the Problem-Solving Agent. In this blog post, we’ll explore what a problem-solving agent is, how it works in AI systems and some exciting real-world applications that showcase its potential. So, buckle up for an insightful journey into the fascinating world of AI problem solvers!

Problem-solving in artificial intelligence can be quite complex, requiring the use of multiple algorithms and data structures. One critical player is the Problem-Solving Agent, which helps machines find solutions to problems. In this blog post, we’ll explore what a problem-solving agent is, how it works in AI systems and some exciting real-world applications that showcase its potential. So, buckle up for an insightful journey into the fascinating world of AI problem solvers!

Table of Contents

What is Problem Solving Agent?

Problem-solving in artificial intelligence is the process of finding a solution to a problem. There are many different types of problems that can be solved, and the methods used will depend on the specific problem. The most common type of problem is finding a solution to a maze or navigation puzzle.

Other types of problems include identifying patterns, predicting outcomes, and determining solutions to systems of equations. Each type of problem has its own set of techniques and tools that can be used to solve it.

There are three main steps in problem-solving in artificial intelligence:

1) understanding the problem: This step involves understanding the specifics of the problem and figuring out what needs to be done to solve it.

2) generating possible solutions: This step involves coming up with as many possible solutions as possible based on information about the problem and what you know about how computers work.

3) choosing a solution: This step involves deciding which solution is best based on what you know about the problem and your options for solving it.

Types of Problem-Solving Agents

Problem-solving agents are a type of artificial intelligence that helps automate problem-solving. They can be used to solve problems in natural language, algebra, calculus, statistics, and machine learning.

There are three types of problem-solving agents: propositional, predicate, and automata. Propositional problem-solving agents can understand simple statements like “draw a line between A and B” or “find the maximum value of x.” Predicate problem-solving agents can understand more complex statements like “find the shortest path between two points” or “find all pairs of snakes in a jar.” Automata is the simplest form of problem-solving agent and can only understand sequences of symbols like “draw a square.”

Classification of Problem-Solving Agents

Problem-solving agents can be classified as general problem solvers or domain-specific problem solvers. General problem solvers can solve a wide range of problems, while domain-specific problem solvers are better suited for solving specific types of problems.

General problem solvers include AI programs that are designed to solve general artificial intelligence (AI) problems such as learning how to navigate a 3D environment or playing games. Domain-specific problem solvers include programs that have been specifically tailored to solve certain types of problems, such as photo editing or medical diagnosis.

Both general and domain-specific problem-solving agents can be used in conjunction with other AI tools, including natural language processing (NLP) algorithms and machine learning models. By combining these tools, we can achieve more effective and efficient outcomes in our data analysis and machine learning processes.

Applications of Problem-Solving Agents

Problem-solving agents can be used in a number of different ways in artificial intelligence. They can be used to help find solutions to specific problems or tasks, or they can be used to generalize a problem and find potential solutions. In either case, the problem-solving agent is able to understand complex instructions and carry out specific tasks.

Problem-solving is an essential skill for any artificial intelligence developer. With AI becoming more prevalent in our lives, it’s important that we have a good understanding of how to approach and solve problems. In this article, we’ll discuss some common problem-solving techniques and provide you with tips on how to apply them when developing AI applications. By applying these techniques systematically, you can build robust AI solutions that work correctly and meet the needs of your users.

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Understanding Problem Solving Agents in Artificial Intelligence

Have you ever wondered how artificial intelligence systems are able to solve complex problems? Problem solving agents play a key role in AI, using algorithms and strategies to find solutions to a variety of challenges.

Problem-solving agents in artificial intelligence are a type of agent that are designed to solve complex problems in their environment. They are a core concept in AI and are used in everything from games like chess to self-driving cars.

In this blog, we will explore problem solving agents in artificial intelligence, types of problem solving agents in AI, real-world applications, and many more.

Table of Contents

What is problem solving agents in artificial intelligence, type 1: simple reflex agents, type 2: model-based agents, type 3: goal-based agents, 2. knowledge base, 3. reasoning engine, 4. actuators, gaming agents, virtual assistants, recommendation systems, scheduling and planning.

A Problem-Solving Agent is a special computer program in Artificial Intelligence. It can perceive the world around it through sensors. Sensors help it gather information.

The agent processes this information using its knowledge base. A knowledge base is like the agent’s brain. It stores facts and rules. Using its knowledge, the agent can reason about the best actions. It can then take those actions to achieve goals.

In simple words, a Problem-Solving Agent observes its environment. It understands the situation. Then it figures out how to solve problems or finish tasks.

These agents use smart algorithms. The algorithms allow them to think and act like humans. Problem-solving agents are very important in AI. They help tackle complex challenges efficiently.

Types of Problem Solving Agents in AI

There are different types of Problem Solving Agents in AI. Each type works in its own way. Below are the different types of problem solving agents in AI:

Simple Reflex Agents are the most basic kind. They simply react to the current situation they perceive. They don’t consider the past or future.

For example, a room thermostat is a Simple Reflex Agent. It turns the heat on or off based only on the current room temperature.

Model-based agents are more advanced. They create an internal model of their environment. This model helps them track how the world changes over time.

Using this model, they can plan ahead for future situations. Self-driving cars use Model-Based Agents to predict how traffic will flow.

Goal-based agents are the most sophisticated type. They can set their own goals and figure out sequences of actions to achieve those goals.

These agents constantly update their knowledge as they pursue their goals. Virtual assistants like Siri or Alexa are examples of Goal-Based Agents assisting us with various tasks.

Each type has its own strengths based on the problem they need to solve. Simple problems may just need Reflex Agents, while complex challenges require more advanced Model-Based or Goal-Based Agents.

Components of a Problem Solving Agent in AI

A Problem Solving Agent has several key components that work together. Let’s break them down:

Sensors are like the agent’s eyes and ears. They collect information from the environment around the agent. For example, a robot’s camera and motion sensors act as sensors.

The Knowledge Base stores all the facts, rules, and information the agent knows. It’s like the agent’s brain full of knowledge. This knowledge helps the agent understand its environment and make decisions.

The Reasoning Engine is the thinking part of the agent. It processes the information from sensors using the knowledge base. The reasoning engine then figures out the best action to take based on the current situation.

Finally, Actuators are like the agent’s hands and limbs. They carry out the actions decided by the reasoning engine. For a robot, wheels and robotic arms would be its actuators.

All these components work seamlessly together. Sensors gather data, the knowledge base provides context, the reasoning engine makes a plan, and actuators implement that plan in the real world.

Real-world Applications of Problem Solving Agents in AI

Problem Solving Agents are not just theoretical concepts. They are actively used in many real-world applications today. Let’s look at some examples:

Problem solving agents are widely used in gaming applications. They can analyze the current game state, consider possible future moves, and make the optimal play. This allows them to beat human players in complex games like chess or go.

Robots in factories and warehouses heavily rely on problem solving agents. These agents perceive the environment around the robot using sensors. They then plan efficient paths and control the robot’s movements and actions accordingly.

Smart home devices like Alexa or Google Home use goal-based problem solving agents. They can understand your requests, look up relevant information from their knowledge base, and provide useful responses to assist you.

Online retailers suggest products you may like based on recommendations from problem solving agents. These agents analyze your past purchases and preferences to make personalized product suggestions.

Scheduling apps help plan your day efficiently using problem solving techniques. The agents consider your appointments, priorities, and travel time to optimize your daily schedule.

Self-Driving Cars One of the most advanced applications is self-driving cars. Their problem solving agents continuously monitor surroundings, predict the movements of other vehicles and objects, and navigate roads safely without human intervention.

In conclusion, Problem solving agents are at the heart of artificial intelligence, mimicking human-like reasoning and decision-making. From gaming to robotics, virtual assistants to self-driving cars, these intelligent agents are already transforming our world. As researchers continue pushing the boundaries, problem solving agents will become even more advanced and ubiquitous in the future. Exciting times lie ahead as we unlock the full potential of this remarkable technology.

Ajay Rathod loves talking about artificial intelligence (AI). He thinks AI is super cool and wants everyone to understand it better. Ajay has been working with computers for a long time and knows a lot about AI. He wants to share his knowledge with you so you can learn too!

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Problem Solving in Artificial Intelligence

The reflex agent of AI directly maps states into action. Whenever these agents fail to operate in an environment where the state of mapping is too large and not easily performed by the agent, then the stated problem dissolves and sent to a problem-solving domain which breaks the large stored problem into the smaller storage area and resolves one by one. The final integrated action will be the desired outcomes.

On the basis of the problem and their working domain, different types of problem-solving agent defined and use at an atomic level without any internal state visible with a problem-solving algorithm. The problem-solving agent performs precisely by defining problems and several solutions. So we can say that problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.  

We can also say that a problem-solving agent is a result-driven agent and always focuses on satisfying the goals.

There are basically three types of problem in artificial intelligence:

1. Ignorable: In which solution steps can be ignored.

2. Recoverable: In which solution steps can be undone.

3. Irrecoverable: Solution steps cannot be undo.

Steps problem-solving in AI: The problem of AI is directly associated with the nature of humans and their activities. So we need a number of finite steps to solve a problem which makes human easy works.

These are the following steps which require to solve a problem :

• Problem definition: Detailed specification of inputs and acceptable system solutions.
• Problem analysis: Analyse the problem thoroughly.
• Knowledge Representation: collect detailed information about the problem and define all possible techniques.
• Problem-solving: Selection of best techniques.

Components to formulate the associated problem: 

• Initial State: This state requires an initial state for the problem which starts the AI agent towards a specified goal. In this state new methods also initialize problem domain solving by a specific class.
• Action: This stage of problem formulation works with function with a specific class taken from the initial state and all possible actions done in this stage.
• Transition: This stage of problem formulation integrates the actual action done by the previous action stage and collects the final stage to forward it to their next stage.
• Goal test: This stage determines that the specified goal achieved by the integrated transition model or not, whenever the goal achieves stop the action and forward into the next stage to determines the cost to achieve the goal.  
• Path costing: This component of problem-solving numerical assigned what will be the cost to achieve the goal. It requires all hardware software and human working cost.

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Table of Contents

What is an agent in ai, the functions of an artificial intelligence agent, the number and types of agents in artificial intelligence, the structure of agents in artificial intelligence, what are agents in artificial intelligence composed of, how to improve the performance of intelligent agents, all about problem-solving agents in artificial intelligence, choose the right program, can you picture a career in artificial intelligence, exploring intelligent agents in artificial intelligence.

Artificial Intelligence, typically abbreviated to AI, is a fascinating field of Information Technology that finds its way into many aspects of modern life. Although it may seem complex, and yes, it is, we can gain a greater familiarity and comfort with AI by exploring its components separately. When we learn how the pieces fit together, we can better understand and implement them.

That’s why today we’re tackling the intelligent Agent in AI. This article defines intelligent agents in Artificial Intelligence , AI agent functions and structure, and the number and types of agents in AI.

Let’s define what we mean by an intelligent agent in AI.

Okay, did anyone, upon hearing the term “intelligent agent,” immediately picture a well-educated spy with a high IQ? No? Anyway, in the context of the AI field, an “agent” is an independent program or entity that interacts with its environment by perceiving its surroundings via sensors, then acting through actuators or effectors.

Agents use their actuators to run through a cycle of perception, thought, and action. Examples of agents in general terms include:

• Software: This Agent has file contents, keystrokes, and received network packages that function as sensory input, then act on those inputs, displaying the output on a screen.
• Human: Yes, we’re all agents. Humans have eyes, ears, and other organs that act as sensors, and hands, legs, mouths, and other body parts act as actuators.
• Robotic: Robotic agents have cameras and infrared range finders that act as sensors, and various servos and motors perform as actuators.

Intelligent agents in AI are autonomous entities that act upon an environment using sensors and actuators to achieve their goals. In addition, intelligent agents may learn from the environment to achieve those goals. Driverless cars and the Siri virtual assistant are examples of intelligent agents in AI.

These are the main four rules all AI agents must adhere to:

• Rule 1: An AI agent must be able to perceive the environment.
• Rule 2: The environmental observations must be used to make decisions.
• Rule 3: The decisions should result in action.
• Rule 4: The action taken by the AI agent must be a rational. Rational actions are actions that maximize performance and yield the best positive outcome.

Artificial Intelligence agents perform these functions continuously:

• Perceiving dynamic conditions in the environment
• Acting to affect conditions in the environment
• Using reasoning to interpret perceptions
• Problem-solving
• Drawing inferences
• Determining actions and their outcomes

There are five different types of intelligent agents used in AI. They are defined by their range of capabilities and intelligence level:

• Reflex Agents: These agents work here and now and ignore the past. They respond using the event-condition-action rule. The ECA rule applies when a user initiates an event, and the Agent turns to a list of pre-set conditions and rules, resulting in pre-programmed outcomes.
• Model-based Agents: These agents choose their actions like reflex agents do, but they have a better comprehensive view of the environment. An environmental model is programmed into the internal system, incorporating into the Agent's history.
• Goal-based agents: These agents build on the information that a model-based agent stores by augmenting it with goal information or data regarding desirable outcomes and situations.
• Utility-based agents: These are comparable to the goal-based agents, except they offer an extra utility measurement. This measurement rates each possible scenario based on the desired result and selects the action that maximizes the outcome. Rating criteria examples include variables such as success probability or the number of resources required.
• Learning agents: These agents employ an additional learning element to gradually improve and become more knowledgeable over time about an environment. The learning element uses feedback to decide how the performance elements should be gradually changed to show improvement.

Agents in Artificial Intelligence follow this simple structural formula:

Architecture + Agent Program = Agent

These are the terms most associated with agent structure:

• Architecture: This is the machinery or platform that executes the agent.
• Agent Function: The agent function maps a precept to the Action, represented by the following formula: f:P* - A
• Agent Program: The agent program is an implementation of the agent function. The agent program produces function f by executing on the physical architecture.

Many AI Agents use the PEAS model in their structure. PEAS is an acronym for Performance Measure, Environment, Actuators, and Sensors. For instance, take a vacuum cleaner.

• Performance: Cleanliness and efficiency
• Environment: Rug, hardwood floor, living room
• Actuator: Brushes, wheels, vacuum bag
• Sensors: Dirt detection sensor, bump sensor

Here’s a diagram that illustrates the structure of a utility-based agent, courtesy of Researchgate.net.

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Agents in Artificial Intelligence contain the following properties:

• Enrironment

Flexibility

• Proactiveness

Using Response Rules

Now, let's discuss these in detail.

Environment

The agent is situated in a given environment.

The agent can operate without direct human intervention or other software methods. It controls its activities and internal environment. The agent independently which steps it will take in its current condition to achieve the best improvements. The agent achieves autonomy if its performance is measured by its experiences in the context of learning and adapting.

• Reactive: Agents must recognize their surroundings and react to the changes within them.
• Proactive: Agents shouldn’t only act in response to their surroundings but also be able to take the initiative when appropriate and effect an opportunistic, goal-directed performance.
• Social: Agents should work with humans or other non-human agents.
• Reactive systems maintain ongoing interactions with their environment, responding to its changes.
• The program’s environment may be guaranteed, not concerned about its success or failure.
• Most environments are dynamic, meaning that things are constantly in a state of change, and information is incomplete.
• Programs must make provisions for the possibility of failure.

Pro-Activeness

Taking the initiative to create goals and try to meet them.

The goal for the agent is directed behavior, having it do things for the user.

• Mobility: The agent must have the ability to actuate around a system.
• Veracity: If an agent’s information is false, it will not communicate.
• Benevolence: Agents don’t have contradictory or conflicting goals. Therefore, every Agent will always try to do what it is asked.
• Rationality: The agent will perform to accomplish its goals and not work in a way that opposes or blocks them.
• Learning: An agent must be able to learn.

When tackling the issue of how to improve intelligent Agent performances, all we need to do is ask ourselves, “How do we improve our performance in a task?” The answer, of course, is simple. We perform the task, remember the results, then adjust based on our recollection of previous attempts.

Artificial Intelligence Agents improve in the same way. The Agent gets better by saving its previous attempts and states, learning how to respond better next time. This place is where Machine Learning and Artificial Intelligence meet.

Problem-solving Agents in Artificial Intelligence employ several algorithm s and analyses to develop solutions. They are:

• Search Algorithms: Search techniques are considered universal problem-solving methods. Problem-solving or rational agents employ these algorithms and strategies to solve problems and generate the best results.

Uninformed Search Algorithms: Also called a Blind search, uninformed searches have no domain knowledge, working instead in a brute-force manner.

Informed Search Algorithms: Also known as a Heuristic search, informed searches use domain knowledge to find the search strategies needed to solve the problem.

• Hill Climbing Algorithms: Hill climbing algorithms are local search algorithms that continuously move upwards, increasing their value or elevation until they find the best solution to the problem or the mountain's peak.

Hill climbing algorithms are excellent for optimizing mathematical problem-solving. This algorithm is also known as a "greedy local search" because it only checks out its good immediate neighbor.

• Means-Ends Analysis: The means-end analysis is a problem-solving technique used to limit searches in Artificial Intelligence programs , combining Backward and Forward search techniques.

The means-end analysis evaluates the differences between the Initial State and the Final State, then picks the best operators that can be used for each difference. The analysis then applies the operators to each matching difference, reducing the current and goal state difference.

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1. What are Intelligent Agents in Artificial Intelligence?

Intelligent Agents in AI are autonomous entities that perceive their environment and make decisions to achieve specific goals.

2. How do Intelligent Agents contribute to AI?

Intelligent Agents enhance AI by autonomously processing information and performing actions to meet set objectives.

3. What are examples of Intelligent Agents in AI?

Examples include recommendation systems, self-driving cars, and voice assistants like Siri or Alexa.

4. How do Intelligent Agents perceive their environment?

Intelligent Agents use sensors to perceive their environment, gathering data for decision-making.

5. What role do Intelligent Agents play in Machine Learning?

In Machine Learning, Intelligent Agents can learn and improve their performance without explicit programming.

6. Are Intelligent Agents the same as AI robots?

Not all Intelligent Agents are robots, but all AI robots can be considered Intelligent Agents.

7. What's the future of Intelligent Agents in AI?

The future of Intelligent Agents is promising, with potential advancements in automation, decision-making, and problem-solving.

8. How do Intelligent Agents impact everyday life?

Intelligent Agents impact our lives by providing personalized recommendations, automating tasks, and enhancing user experiences.

9. How do Intelligent Agents make decisions in AI?

Intelligent Agents make decisions based on their perception of the environment and pre-defined goals.

10. Can anyone use Intelligent Agents in AI?

Yes, anyone with the right tools and understanding can utilize Intelligent Agents in AI.

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What Is Problem Solving Agent In Artificial Intelligence

What Is Problem Solving Agent In Artificial Intelligence- Artificial Intelligence (AI) is a field that is always changing. One important part of AI is agents that can solve difficult problems using computers. In the field of artificial intelligence , a problem-solving agent is a complex program or system that can think about, understand, and come up with the best solutions for a wide range of situations. At their core, these agents try to mimic human skills for solving problems, adapting to new situations, and making difficult choices.

Problem-solving agents’ main job is to look into and weigh all of their options on their own before deciding what the best thing to do is in a given scenario. The agent can understand problems, come up with solutions, and keep changing its method based on feedback and changes in its environment by using a variety of algorithms, heuristics, and ways of reasoning.

In many areas of artificial intelligence, like robots, expert systems, and data analytics, problem-solving agents are used a lot. They are flexible and can be used in many places. This overview will go over the details of problem-solving agents, including their architecture, how they make decisions, and how they have helped artificial intelligence grow over time. By looking closely, we hope to show the subtleties of how these smart animals help solve problems in the ever-changing area of artificial intelligence.

What is problem-solving with artificial intelligence?

Artificial intelligence (AI) problem-solving often involves investigating potential solutions to problems through reasoning techniques, making use of polynomial and differential equations, and carrying them out and use modelling frameworks.

It is called artificial intelligence (AI) problem-solving, when computer tools are used to deal with difficult problems in a way that is similar to how humans solve problems. AI-assisted problem-solving basically means creating and using intelligent agents, which are computer programs that can understand, analyze, and come up with the best answers for any given situation.

Expert systems, machine learning methods, and rule-based systems are just some of the ways that AI solves problems. By imitating how humans think, these systems let computers learn from data, spot trends, and come to logical conclusions. AI programs learn how to solve problems by working with large datasets. This helps them find patterns, correlations, and links that normal programming methods might miss.

One interesting thing about AI-driven problem-solving is that it can be used in many different fields, like manufacturing, logistics, healthcare, and banking. For instance, machine learning systems can find health risks, predict market trends, make production more efficient, and find ways to make things cheaper. Iterative AI algorithms let systems learn and get better all the time. This means that over time, these systems change and get better at dealing with more difficult problems.

Artificial intelligence problem-solving uses the computing power of intelligent agents to help computers get through tough situations, make good choices, and make a real difference in a wide range of real-world problems.

What is the primary function of a problem-solving agent in the context of artificial intelligence?

In artificial intelligence, the main goal of a problem-solving agent is to copy human thought processes so that it can explore and solve difficult situations on its own in a certain area. These intelligent living things can understand and analyze information from their surroundings and come up with the best answers. A big part of how they work is that they can see the problem for what it is, frame the issue, and follow strict steps to find solutions that will work.

Agents that solve problems use heuristics or algorithms to look into possible solution spaces and weigh the pros and cons of different action plans to get to the state they want. The decision-making process involves picking the best thing to do based on how well the agent understands the situation and how they think about it.

Learning methods are often built into problem-solving agents, which lets them change and get better over time. Their ability to learn makes it easier for them to deal with new problems and change how they do things based on feedback and experience. Artificial intelligence apps are built around problem-solving agents, which can be used in robotics, expert systems, or data analytics. They make AI more flexible, useful, and potentially revolutionary in many areas of problem-solving.

What are the main functions of problem-solving agent?

The problem-solving agent performs precisely by defining problems and several solutions. So we can say that problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.

Problem-solving robots that use artificial intelligence are made to do a wide range of important tasks, showing that they can get around and solve problems.

Perception: The agent needs to use sensors or data inputs to notice and understand what’s going on around it. Understanding how the problem is currently standing takes gathering the right information.

Problem formulation: Once the person has collected the data, they need to figure out what the problem is. Setting a goal or solution state and knowing the steps that can be taken to get there are part of this.

Search: The problem-solving agent searches to find the best answer. This includes looking into possible action plans or ways to get from where things are now to where you want them to be in the future.

Reasoning and Making Decisions: The agent uses reasoning to weigh the pros and cons of different acts. It has to make choices based on the facts it has access to, taking into account people’s preferences, limitations, and the general way it plans to solve the problem.

The agent does certain things to change the system or surroundings after deciding what the best course of action is. For a software-based agent, these could be metaphorical actions. For a robotic agent, these could be real actions.

Learning: A lot of problem-solving agents can learn from their mistakes. Through feedback systems, they can change and improve their methods over time, which will make them better at solving similar problems in the future.

When put together, these skills let AI problem-solving bots handle a lot of different kinds of problems on their own in many different areas of artificial intelligence.

What are the steps taken by problem-solving agent?

A problem-solving agent has three phases: • problem formulation, searching solution and executing actions in the solution. A problem can be defined by five components: • initial state, actions, transition model, goal test, path cost.

A problem-solving AI uses a methodical technique to get around and solve tough problems. These actions show how well the agent can understand, plan for, and change to its surroundings.

Perception: Using sensors or data sources, the agent figures out what’s going on around it and gathers information that helps it understand what’s going on.

Issue formulation is the process of defining an issue by listing the end goal or desired result and the possible paths that could be taken to get from where things are now to where they need to be.

Setting Goals: The agent sets goals for itself to work toward, which guides its efforts to solve problems.

Search and Exploration: The agent uses algorithms or heuristics to look for possible answers as it moves around the problem area. To do this, you have to look at a lot of action scenes to find the best or most enjoyable one.

Making Choices: The agent uses its reasoning to look at all of the options and chooses a plan of action, taking into account things like cost, effectiveness, and practicality.

Execution: The actions that were chosen are carried out, which changes the system or surroundings. Doing this task is very important for getting closer to the goal.

Input and Learning: The agent can learn and change by being told what happened when it did something. Learning processes help the agent solve problems in the future by letting it get better at what it does over time.

Iteration: For most problems, you have to do the same steps over and over again. As feedback comes in and the problem area changes, the agent may look at its approach and make changes to it.

When you put these steps together, you get a dynamic and adaptable process that lets AI problem-solving agents do a lot of different jobs in many different areas.

How does a problem-solving agent perceive and interpret information from its environment?

A problem-solving agent uses different sensors, data inputs, and processing methods to pick up on and understand information in its environment. The method is customized to meet the needs of the AI system, but it is similar to how humans perceive things.

Sensor Inputs: The robot has sensors that gather information about its surroundings. If the situation calls for it, these monitors could be cameras, microphones, touch sensors, or other specialized gadgets.

Representing Data: The sensors gather raw data, which is then changed into a shape that the agent can understand and use. To do this, sensory information needs to be turned into a structured form that an AI system can control.

The method of taking out important environmental traits from data is called feature extraction. Part of this process is for the agent to look for patterns, shapes, sounds, or other useful information that helps them understand what’s going on.

In this step, the agent puts the retrieved features in the context of the issue space so that the interpreted data matches what it knows about the world. This step is very important for the agent to understand the data and know how it fits into the bigger picture of fixing the problem.

Internal Representation: Once the data has been interpreted, it is used to change an agent’s internal representation. This creates a model of the current situation. This idea forms the basis for later methods for making decisions and fixing problems.

Problem-solving agents can take in and understand information from their surroundings through perception and understanding. This helps them make good decisions and take action.

What do you mean by problem-solving?

Problem solving is the act of defining a problem; determining the cause of the problem; identifying, prioritizing, and selecting alternatives for a solution; and implementing a solution.

People or systems solve problems by analyzing, planning, and carrying out actions that will help them reach their goals or get past a certain obstacle. It’s what makes people smart and a big topic of study in many areas, like psychology, education, and even artificial intelligence.

Problem-solving means recognizing and explaining a problem, understanding its surroundings and limitations, and coming up with workable solutions. Critical thought, logical reasoning, creativity, and the ability to make decisions are usually needed for this process. Thinking of ways to solve problems is a skill that can be used in many situations and not just in one industry.

In the area of artificial intelligence, problem-solving means making and using algorithms and smart systems that can figure out hard problems on their own. These computers, which are called “problem-solving agents,” use computer techniques to copy the way people solve problems by processing data, thinking about different options, and making choices.

Problem-solving that works, whether done by humans or machines, depends on how well understanding, planning, doing, and learning all work together. This skill shows that both people and smart systems can naturally get past problems and get the best results. It encourages new ideas, flexibility, and progress in many areas.

There are three main steps in problem-solving in artificial intelligence:

When working on problems in artificial intelligence, there are usually three main steps: describing the problem, looking for a solution, and putting the answer into action.

As the first step, problem representation turns the real-world problem into a form that a machine or algorithm can understand. You need to describe the problem space, the starting and ending states, and any possible operators or acts that could be used to change one state to another. The image is what other computer processes are built on top of.

Search: Once the AI system has found a good way to represent the problem, it looks through the problem space for possible answers. Different search algorithms are used to sort through the options. These include heuristic-based methods like A* search, depth-first search, and breadth-first search. The idea is to go through and evaluate different paths in the problem space over and over again until you find the best or most satisfactory solution.

Putting the Solution into Action: Once the AI system has found a good solution through the search process, it does the set of tasks needed to move from the starting state to the ending state. Doing the suggested actions in a real or simulated setting, making the needed changes, and solving the problem successfully are all part of this step.

When you put these three steps together, they make an organized framework for AI problem-solving that lets smart agents move through tough problem domains and come up with answers that work.

Problem Solving Agents in Artificial Intelligence

AI problem-solving agents are smart machines that can figure out how to solve hard problems and find the best answers in certain areas. The main ideas of artificial intelligence are summed up by these agents, which use computer methods to solve problems like humans. Problem-solving agents play a part in a number of basic steps that can be used to sum them up.

These agents perceive the world around them by using sensors or data sources to find out what’s going on. The next step is problem formulation, which means describing the issue by figuring out what the desired state is and what possible actions or operators could be used to get there.

The agent then uses search algorithms to move around and look into the problem space. It judges possible answers using heuristics or systematic exploration. Based on the available information and the agent’s mental picture of the situation, the goal of the reasoning and decision-making process is to choose the most likely next step.

After picking a solution, the problem-solving agent goes to action implementation and follows the given steps to change the world in the way that was wanted. Lastly, these agents change and improve over time by using learning processes to make their problem-solving skills better based on feedback and experience.

Problem-solving bots are very important to the progress of artificial intelligence in robotics, expert systems, and data analytics. They work well and can be changed to fit a lot of different situations and problems.

Agents that can solve problems are very important for understanding artificial intelligence. Because they can mimic the cognitive processes that humans have, these agents have become very important in solving many problems in many areas. As we learn more about AI, it becomes clear that problem-solving agents are not just computer programs; they are the building blocks of smart systems that can change to new situations and make the best decisions.

Agents who can deal with tough problems are valuable because they can come up with creative answers even when things aren’t clear or simple. They can be used for many things, from helping experts understand complicated subjects to letting robots make decisions on their own in changing settings. Their way of handling problems is iterative, and they usually use algorithms and heuristics to help them. This shows that the system is always learning, similar to how humans solve problems by adapting and being strong.

AI’s problem-solving bots have a bright future because machine learning, deep learning, and reinforcement learning are always getting better. When these tools are used together, they help people solve problems in ways that were previously unimaginable and in a wider range of fields. The journey of issue-solving bots shows how artificial intelligence (AI) can change things, opening up new areas and redefining how smart people can solve problems in the digital age.

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Problem Solving Agents in Artificial Intelligence

In this post, we will talk about Problem Solving agents in Artificial Intelligence, which are sort of goal-based agents. Because the straight mapping from states to actions of a basic reflex agent is too vast to retain for a complex environment, we utilize goal-based agents that may consider future actions and the desirability of outcomes.

You Will Learn

Problem Solving Agents

Problem Solving Agents decide what to do by finding a sequence of actions that leads to a desirable state or solution.

An agent may need to plan when the best course of action is not immediately visible. They may need to think through a series of moves that will lead them to their goal state. Such an agent is known as a problem solving agent , and the computation it does is known as a search .

The problem solving agent follows this four phase problem solving process:

• Goal Formulation: This is the first and most basic phase in problem solving. It arranges specific steps to establish a target/goal that demands some activity to reach it. AI agents are now used to formulate goals.
• Problem Formulation: It is one of the fundamental steps in problem-solving that determines what action should be taken to reach the goal.
• Search: After the Goal and Problem Formulation, the agent simulates sequences of actions and has to look for a sequence of actions that reaches the goal. This process is called search, and the sequence is called a solution . The agent might have to simulate multiple sequences that do not reach the goal, but eventually, it will find a solution, or it will find that no solution is possible. A search algorithm takes a problem as input and outputs a sequence of actions.
• Execution: After the search phase, the agent can now execute the actions that are recommended by the search algorithm, one at a time. This final stage is known as the execution phase.

Problems and Solution

Before we move into the problem formulation phase, we must first define a problem in terms of problem solving agents.

A formal definition of a problem consists of five components:

Initial State

Transition model.

It is the agent’s starting state or initial step towards its goal. For example, if a taxi agent needs to travel to a location(B), but the taxi is already at location(A), the problem’s initial state would be the location (A).

It is a description of the possible actions that the agent can take. Given a state s, Actions ( s ) returns the actions that can be executed in s. Each of these actions is said to be appropriate in s.

It describes what each action does. It is specified by a function Result ( s, a ) that returns the state that results from doing action an in state s.

The initial state, actions, and transition model together define the state space of a problem, a set of all states reachable from the initial state by any sequence of actions. The state space forms a graph in which the nodes are states, and the links between the nodes are actions.

It determines if the given state is a goal state. Sometimes there is an explicit list of potential goal states, and the test merely verifies whether the provided state is one of them. The goal is sometimes expressed via an abstract attribute rather than an explicitly enumerated set of conditions.

It assigns a numerical cost to each path that leads to the goal. The problem solving agents choose a cost function that matches its performance measure. Remember that the optimal solution has the lowest path cost of all the solutions .

Example Problems

The problem solving approach has been used in a wide range of work contexts. There are two kinds of problem approaches

• Standardized/ Toy Problem: Its purpose is to demonstrate or practice various problem solving techniques. It can be described concisely and precisely, making it appropriate as a benchmark for academics to compare the performance of algorithms.
• Real-world Problems: It is real-world problems that need solutions. It does not rely on descriptions, unlike a toy problem, yet we can have a basic description of the issue.

Some Standardized/Toy Problems

Vacuum world problem.

Let us take a vacuum cleaner agent and it can move left or right and its jump is to suck up the dirt from the floor.

The vacuum world’s problem can be stated as follows:

States: A world state specifies which objects are housed in which cells. The objects in the vacuum world are the agent and any dirt. The agent can be in either of the two cells in the simple two-cell version, and each call can include dirt or not, therefore there are 2×2×2 = 8 states. A vacuum environment with n cells has n×2 n states in general.

Initial State: Any state can be specified as the starting point.

Actions: We defined three actions in the two-cell world: sucking, moving left, and moving right. More movement activities are required in a two-dimensional multi-cell world.

Transition Model: Suck cleans the agent’s cell of any filth; Forward moves the agent one cell forward in the direction it is facing unless it meets a wall, in which case the action has no effect. Backward moves the agent in the opposite direction, whilst TurnRight and TurnLeft rotate it by 90°.

Goal States: The states in which every cell is clean.

Action Cost: Each action costs 1.

8 Puzzle Problem

In a sliding-tile puzzle , a number of tiles (sometimes called blocks or pieces) are arranged in a grid with one or more blank spaces so that some of the tiles can slide into the blank space. One variant is the Rush Hour puzzle, in which cars and trucks slide around a 6 x 6 grid in an attempt to free a car from the traffic jam. Perhaps the best-known variant is the 8- puzzle (see Figure below ), which consists of a 3 x 3 grid with eight numbered tiles and one blank space, and the 15-puzzle on a 4 x 4  grid. The object is to reach a specified goal state, such as the one shown on the right of the figure. The standard formulation of the 8 puzzles is as follows:

STATES : A state description specifies the location of each of the tiles.

INITIAL STATE : Any state can be designated as the initial state. (Note that a parity property partitions the state space—any given goal can be reached from exactly half of the possible initial states.)

ACTIONS : While in the physical world it is a tile that slides, the simplest way of describing action is to think of the blank space moving Left , Right , Up , or Down . If the blank is at an edge or corner then not all actions will be applicable.

TRANSITION MODEL : Maps a state and action to a resulting state; for example, if we apply Left to the start state in the Figure below, the resulting state has the 5 and the blank switched.

GOAL STATE :  It identifies whether we have reached the correct goal state. Although any state could be the goal, we typically specify a state with the numbers in order, as in the Figure above.

ACTION COST : Each action costs 1.

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Digital image processing: all you need to know.

Published:  06 August 2024 Contributor : Anna Gutowska

A multiagent system (MAS) consists of multiple artificial intelligence (AI) agents working collectively to perform tasks on behalf of a user or another system.

Each agent within a MAS has individual properties but all agents behave collaboratively to lead to desired global properties. 1 Multiagent systems are valuable in completing large-scale, complex tasks that can encompass hundreds, if not thousands, of agents. 2

Central to this idea are artificial intelligence (AI)  agents. An AI agent refers to a system or program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and using available tools. At the core of AI agents are  large language models (LLMs) . These intelligent agents leverage the advanced natural language processing techniques of LLMs to comprehend and respond to user inputs. Agents work through problems step-by-step and determine when to call on external tools. What differentiates AI agents from traditional LLMs is the use of tools and the ability to design a plan of action. The tools available to an agent can include external datasets, web searches and application programming interfaces (APIs). Similarly to human decision-making, AI agents can also update their memory as they acquire new information. The information-sharing, tool usage and adaptive learning allow AI agents to be more general purpose than traditional LLMs.

For more information about single agent systems, see our detailed AI agent content . 

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

Single agent intelligent systems engage with their environment to autonomously plan, call tools and produce responses. The tools made available to an agent provide information that is otherwise unavailable to the agent. As previously described, this information can be a database acquired through an API or another agent. There is a distinction here between single and multiagent systems. When calling another agent as a tool, that secondary agent is part of the original agent’s environmental stimuli. That information is acquired and no further cooperation takes place. Whereas multiagent systems differ by involving all agents within the environment to model each other’s goals, memory and plan of action. 4 Communication between agents can be direct or indirect through altering the shared environment.

Each entity within a multiagent system is an autonomous agent to some extent. This autonomy is typically seen by the agent’s planning, tool calling and general reasoning. In a multiagent system, agents remain autonomous but also cooperate and coordinate in agent structures. 3 To solve complex problems, agent communication and distributed problem-solving are key. This type of agent interaction can be described as multiagent  reinforcement learning . The information shared through this form of learning can include instantaneous information acquired through sensors or actions. Additionally, an agent’s experiences in the form of episodic information can be shared. These episodes can be sequences of sensations, actions and learned policies. Finally, agents can share their experiences in real-time to prevent other agents from repetitively learning the same policies. 5

Individual agents are powerful on their own. They can create subtasks, use tools and learn through their interactions. The collective behavior of multiagent systems increases the potential for accuracy, adaptability and scalability. Multiagent systems tend to outperform single-agent systems due to the larger pool of shared resources, optimization and automation. Instead of multiple agents learning the same policies, one can share learned experiences to optimize time complexity and efficiency. 5

Centralized networks

Multiagent systems can operate under various architectures. In centralized networks, a central unit contains the global knowledge base, connects the agents and oversees their information. A strength of this structure is the ease of communication between agents and uniform knowledge. A weakness of the centrality is the dependence on the central unit; if it fails, the entire system of agents fails. 6

Decentralized networks

Agents in decentralized networks share information with their neighboring agents instead of a global knowledge base. Some benefits of decentralized networks are robustness and modularity. The failure of one agent does not cause the overall system to fail since there is no central unit. One challenge of decentralized agents is coordinating their behavior to benefit other cooperating agents. 7

There are also many ways of organizing agents within a multiagent system including:

Hierarchical structure

A hierarchical structure is tree-like and contains agents with varying levels of autonomy. Within a simple hierarchical structure, one agent can have the decision-making authority. In a uniform hierarchical structure, the responsibility can be distributed among multiple agents. 8

Holonic structure

Within this architecture type, agents are grouped into holarchies. A holon is an entity that cannot operate without its components. For instance, the human body is a holon because it cannot function without working organs. 9 Similarly, in holonic multiagent systems, the leading agent can have multiple subagents while appearing to be a singular entity. 8 These subagents can also play roles in other holons. These hierarchical structures are self-organized and created to achieve a goal through the collaboration of the subagents.

Coalition structure

Coalitions are helpful in cases of underperforming singular agents in a group. In these situations, agents temporarily unite to boost utility or performance. Once the desired performance is reached, the coalitions are disbursed. It can become difficult to maintain these coalitions in dynamic environments. Regrouping is often necessary to enhance performance. 9

Teams are similar in structure to coalitions. In teams, agents cooperate to improve the performance of the group. Agents in teams do not work independently, unlike in coalitions. Agents in teams are much more dependent on one another and their structure is more hierarchical than coalitions. 8

The behaviors of agents within a multiagent system often reflect behaviors occurring in nature. The following agent behaviors can apply to both multisoftware and multirobot agents.

The collective behavior seen in multiagent systems can resemble that of birds, fish and humans. In these systems, agents share an objective and require some organization to coordinate their behavior. Flocking pertains to directional synchronization and the structure of these flocks can be described by these heuristics:  10

• Separation: attempt to avoid collision with nearby agents.
• Alignment: attempt to match the velocity of nearby agents.
• Cohesion: attempt to remain close to other agents.

In the context of software agents, this coordination is crucial for multiagent systems managing transportation networks such as railroad systems.

The spatial positioning of agents in a multiagent system can be compared to the swarming that occurs in nature. For instance, birds fly in sync by adjusting to neighboring birds. From a technical perspective, swarming is the emergent self-organization and aggregation among software agents with decentralized control. 11 A benefit of swarming is that one operator can be trained to manage a swarm of agents. This method is less computationally expensive and more reliable than training an operator for each agent. 12

Multiagent systems can solve many complex, real-world tasks. Some examples of applicable domains include:

Multiagent systems can be used to manage transportation systems. The qualities of multiagent systems that allow for the coordination of complex transportation systems are communication, collaboration, planning and real-time information access. Examples of distributed systems that might benefit from MAS are railroad systems, truck assignments and marine vessels visiting the same ports. 13

Multiagent systems can be used for various specific tasks in the healthcare field. These agent-based systems can aid in disease prediction and prevention through genetic analysis. Medical research about cancer might be one application. 14 In addition, multiagent systems can serve as tools for preventing and simulating epidemic spread. This forecasting is made possible by using epidemiologically informed neural networks and machine learning (ML) techniques to manage large datasets. These findings can affect public health and public policy. 15

Numerous factors affect a supply chain. These factors range from the creation of goods to the consumer purchase. Multiagent systems can use their vast informational resources, versatility and scalability to connect the components of supply chain management . To best navigate this intelligent automation , virtual agents should negotiate with one another. This negotiation is important for agents collaborating with other agents that have conflicting goals. 16

Multiagent systems can aid in strengthening defense systems. Potential threats can include both physical national security issues and cyberattacks. Multiagent systems can use their tools to simulate potential attacks. One example is a maritime attack simulation. This scenario would involve agents working in teams to capture the interactions between encroaching terrorist boats and defense vessels. 17  Also, by working in cooperative teams, agents can monitor different areas of the network to detect incoming threats such as distributed denial of service (DDoS)  flooding attacks. 18

There are several characteristics of multiagent systems that provide advantages including:

Flexibility

Multiagent systems can adjust to varying environments by adding, removing or adapting agents.

Scalability

The cooperation of several agents allows for a greater pool of shared information. This collaboration allows multiagent systems to solve more complex problems and tasks than single-agent systems.

Domain specialization

Single agent systems require one agent to perform tasks in various domains, whereas each agent in a multiagent system can hold specific domain expertise.

Greater performance

Multiagent frameworks tend to outperform singular agents. 19 This is because the more action plans are available to an agent, the more learning and reflection occur. An AI agent incorporating knowledge and feedback from other AI agents with specialties in related areas can be useful for information synthesis. This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence.

There are several challenges in designing and implementing multiagent systems including:

Agent malfunctions

Multiagent systems built on the same  foundation models  can experience shared pitfalls. Such weaknesses might cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks. 20  This highlights the importance of data governance in building foundation models and the need for thorough training and testing processes.

Coordination complexity

One of the greatest challenges with building multiagent systems is developing agents that can coordinate and negotiate with one another. This cooperation is essential for a functioning multiagent system.

Unpredictable behavior

The agents performing autonomously and independently in decentralized networks can experience conflicting or unpredictable behavior. Detecting and managing issues within the larger system might be difficult under these conditions.

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Climate change is posing challenges for operating and designing critical infrastructure. Increasingly, AI has been used to enhance these decision making process.

An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.

Conversational artificial intelligence (AI) refers to technologies, such as chatbots or virtual agents, that users can talk to. They combine natural language processing (NLP) with machine learning to help imitate human interactions.

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1  Edmund H. Durfee and Jeffrey S. Rosenschein, "Distributed problem solving and multi-agent systems: Comparisons and examples." In Proceedings of the Thirteenth International Distributed Artificial Intelligence Workshop , 1994,  https://aaai.org/papers/000-ws94-02-004/ (link resides outside ibm.com)

² David Kinny and Michael Georgeff, "Modelling and design of multi-agent systems," International Workshop on Agent Theories, Architectures, and Languages , 1996, https://link.springer.com/chapter/10.1007/BFb0013569  (link resides outside ibm.com)

³ Michael Wooldridge, An introduction to multiagent systems . John Wiley & Sons, 2009, https://dl.acm.org/doi/10.5555/1695886 (link resides outside ibm.com)

⁴ Peter Stone and Manuela Veloso, “Multiagent Systems: A Survey from a Machine Learning Perspective,” Autonomous Robotics, 2000, https://link.springer.com/article/10.1023/A:1008942012299  (link resides outside ibm.com)

⁵ Ming Tan, “Multi-Agent Reinforcement Learning: Independent versus Cooperative Agent,” Proceedings of the tenth international conference on machine learning, 1993, https://web.media.mit.edu/~cynthiab/Readings/tan-MAS-reinfLearn.pdf (link resides outside ibm.com)

⁶ Jianan Wang, Chunyan Wang, Ming Xin, Zhengtao Ding and Jiayuan Shan, Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective , Academic Press, 2020, https://www.sciencedirect.com/book/9780128201183/cooperative-control-of-multi-agent-systems?via=ihub=  (link resides outside ibm.com)

⁷ Lucian Busoniu, Bart De Schutter and Robert Babuska, “Decentralized reinforcement learning control of a robotic manipulator,” Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision , 2006,  https://ieeexplore.ieee.org/document/4150192  (link resides outside ibm.com)

⁸ Parasumanna Gokulan Balaji and Dipti Srinivasan, "An Introduction to Multi-Agent Systems,” Innovations in Multi-Agent Systems and Applications - 1 , 2010, https://link.springer.com/chapter/10.1007/978-3-642-14435-6_1  (link resides outside ibm.com)

⁹ Vincent Hilaire, Abder Koukam and Sebastian Rodriguez, "An adaptative agent architecture for holonic multi-agent systems," ACM Transactions on Autonomous and Adaptive Systems (TAAS) , 2008,  https://dl.acm.org/doi/10.1145/1342171.1342173  (link resides outside ibm.com)

¹⁰ Reza Olfati-Saber, “Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory,” EEE Transactions on automatic control 51, no. 3, 2006, https://ieeexplore.ieee.org/document/1605401  (link resides outside ibm.com)

¹¹ H. Van Dyke Parunak and Sven A. Brueckner, "Engineering swarming systems," Methodologies and software engineering for agent systems , 2004, https://link.springer.com/chapter/10.1007/1-4020-8058-1_21  (link resides outside ibm.com)

¹² Ross Arnold, Kevin Carey, Benjamin Abruzzo and Christopher Korpela, "What is a robot swarm: a definition for swarming robotics," IEEE 10th annual ubiquitous computing, electronics & mobile communication conference (uemcon) , 2019, https://ieeexplore.ieee.org/document/8993024  (link resides outside ibm.com)

¹³ Hans Moonen, Multi-agent systems for transportation planning and coordination, 2009.

¹⁴ Elhadi Shakshuki and Malcolm Reid, “Multi-Agent System Applications in Healthcare: Current Technology and Future Roadmap,” Procedia Comput Sci, 2015, https://www.sciencedirect.com/science/article/pii/S1877050915008716?via%3Dihub  (link resides outside ibm.com)

¹⁵ Alexander Rodríguez, "AI & Multi-agent Systems for Data-centric Epidemic Forecasting," AAMAS , 2023, https://dl.acm.org/doi/10.5555/3545946.3599132  (link resides outside ibm.com)

¹⁶ Ksenija Mandic and Boris Delibašić, “Application Of Multi-Agent Systems In Supply Chain Management,” Management Journal of Sustainable Business and Management Solutions in Emerging Economies , 2012,  https://scindeks.ceon.rs/article.aspx?artid=0354-86351263075M (link resides outside ibm.com)

¹⁷ Thomas W. Lucas, Susan M. Sanchez, Lisa R. Sickinger, Felix Martinez and Jonathan W. Roginski, 2007 Winter Simulation Conference , 2007, https://ieeexplore.ieee.org/document/4419596  (link resides outside ibm.com)

¹⁸ Igor Kotenko, Multi-agent Modelling and Simulation of Cyber-Attacks and Cyber-Defense for Homeland Security, IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007, https://ieeexplore.ieee.org/document/4488494 (link resides outside ibm.com)

¹⁹ Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu and Deheng Ye. "More agents is all you need."  arXiv preprint, 2024,  https://arxiv.org/abs/2402.05120   (link resides outside ibm.com)

²⁰ Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim and Markus Anderljung, “Visibility into AI Agents,” The 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024,  https://arxiv.org/abs/2401.13138  (link resides outside ibm.com)

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