Box Of Notes

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 state space graph for the two-cell vacuum world.

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.

A typical instance of the 8-puzzle

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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Problem Solving

Definitions.

Searching is one of the classic areas of AI.

A problem is a tuple $(S, s, A, \rho, G, P)$ where

Example: A water jug problem

You have a two-gallon jug and a one-gallon jug; neither have any measuring marks on them at all. Initially both are empty. You need to get exactly one gallon into the two-gallon jug. Formally:

A graphical view of the transition function (initial state shaded, goal states outlined bold):

water21.png

And a tabular view:

To solve this problem, an agent would start at the initial state and explore the state space by following links until it arrived in a goal state. A solution to the water jug problem is a path from the initial state to a goal state .

Example solutions

There are an infinite number of solutions. Sometimes we are interested in the solution with the smallest path cost; more on this later.

Awww Man.... Why are we studying this?

Even if they’re not completely right, there are still zillions of problems that can be formulated in problem spaces, e.g.

Problem Types

State finding vs. action sequence finding.

A fundamental distinction:

Offline vs. Online Problems

In an online problem, the agent doesn’t even know what the state space is, and has to build a model of it as it acts. In an offline problem, percepts don’t matter at all. An agent can figure out the entire action sequence before doing anything at all .

Offline Example : Vacuum World with two rooms, cleaning always works, a square once cleaned stays clean. States are 1 – 8, goal states are 1 and 5.

vacuumstate.png

Sensorless (Conformant) Problems

The agent doesn’t know where it is. We can use belief states (sets of states that the agent might be in). Example from above deterministic, static, single-agent vacuum world:

Note the goal states are 1 and 5. If a state 15 was reachable, it would be a goal too.

Contingency Problems

Contingency Problem: The agent doesn’t know what effect its actions will have. This could be due to the environment being partially observable, or because of another agent. Ways to handle this:

Example: Partially observable vacuum world (meaning you don’t know the status of the other square) in which sucking in a clean square may make it dirty.

Can also model contingency problems is with "AND-OR graphs".

Example: find a winning strategy for Nim if there are only five stones in one row left. You are player square. You win if it is player circle’s turn with zero stones left.

nim.png

In general then, a solution is a subtree in which

If the tree has only OR nodes, then the solution is just a path.

Search Algorithms

Hey, we know what a problem is, what a problem space is, and even what a solution is, but how exactly do we search the space ? Well there are zillions of approaches:

Types of Problem Solving Tasks

Agents may be asked to be

An algorithm is

Search Trees

Example: The water jug problem with 4 and 3 gallon jugs. Cost is 1 point per gallon used when filling, 1 point to make a transfer, 5 points per gallon emptied (since it makes a mess). The search tree might start off like this:

jug43tree.png

Search trees have

The complexity of most search algorithms can be written as a function of one or more of $b$, $d$ and $m$.

In general though there may be more states than there are fundamental particles in the universe. But we need to find a solution. Usually is helpful to

define problem solving agent

Problem-Solving Agents In Artificial Intelligence

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.

Hridhya Manoj

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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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.

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.

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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.

Intelligent_Agents

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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Introduction to Artificial Intelligence: A Beginner's Guide

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Agents in Artificial Intelligence

In artificial intelligence, an agent is a computer program or system that is designed to perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.

Agents can be classified into different types based on their characteristics, such as whether they are reactive or proactive, whether they have a fixed or dynamic environment, and whether they are single or multi-agent systems.

  • Reactive agents are those that respond to immediate stimuli from their environment and take actions based on those stimuli. Proactive agents, on the other hand, take initiative and plan ahead to achieve their goals. The environment in which an agent operates can also be fixed or dynamic. Fixed environments have a static set of rules that do not change, while dynamic environments are constantly changing and require agents to adapt to new situations.
  • Multi-agent systems involve multiple agents working together to achieve a common goal. These agents may have to coordinate their actions and communicate with each other to achieve their objectives. Agents are used in a variety of applications, including robotics, gaming, and intelligent systems. They can be implemented using different programming languages and techniques, including machine learning and natural language processing.

Artificial intelligence is defined as the study of rational agents. A rational agent could be anything that makes decisions, such as a person, firm, machine, or software. It carries out an action with the best outcome after considering past and current percepts(agent’s perceptual inputs at a given instance). An AI system is composed of an agent and its environment . The agents act in their environment. The environment may contain other agents. 

An agent is anything that can be viewed as:

  • Perceiving its environment through sensors and
  • Acting upon that environment through actuators
Note : Every agent can perceive its own actions (but not always the effects).

Interaction of Agents with Environment

Interaction of Agents with the Environment

Structure of an AI Agent

To understand the structure of Intelligent Agents, we should be familiar with Architecture and Agent programs. Architecture is the machinery that the agent executes on. It is a device with sensors and actuators, for example, a robotic car, a camera, and a PC. An agent program is an implementation of an agent function. An agent function is a map from the percept sequence(history of all that an agent has perceived to date) to an action.   

Agent = Architecture + Agent Program

There are many examples of agents in artificial intelligence. Here are a few:

  • Intelligent personal assistants: These are agents that are designed to help users with various tasks, such as scheduling appointments, sending messages, and setting reminders. Examples of intelligent personal assistants include Siri, Alexa, and Google Assistant.
  • Autonomous robots: These are agents that are designed to operate autonomously in the physical world. They can perform tasks such as cleaning, sorting, and delivering goods. Examples of autonomous robots include the Roomba vacuum cleaner and the Amazon delivery robot.
  • Gaming agents: These are agents that are designed to play games, either against human opponents or other agents. Examples of gaming agents include chess-playing agents and poker-playing agents.
  • Fraud detection agents: These are agents that are designed to detect fraudulent behavior in financial transactions. They can analyze patterns of behavior to identify suspicious activity and alert authorities. Examples of fraud detection agents include those used by banks and credit card companies.
  • Traffic management agents: These are agents that are designed to manage traffic flow in cities. They can monitor traffic patterns, adjust traffic lights, and reroute vehicles to minimize congestion. Examples of traffic management agents include those used in smart cities around the world.
  • A software agent has Keystrokes, file contents, received network packages that act as sensors and displays on the screen, files, and sent network packets acting as actuators.
  • A Human-agent has eyes, ears, and other organs which act as sensors, and hands, legs, mouth, and other body parts act as actuators.
  • A Robotic agent has Cameras and infrared range finders which act as sensors and various motors act as actuators.   

Characteristics of an Agent

Characteristics of an Agent

Types of Agents

Agents can be grouped into five classes based on their degree of perceived intelligence and capability :

Simple Reflex Agents

Model-Based Reflex Agents

Goal-Based Agents

Utility-Based Agents

Learning Agent

  • Multi-agent systems
  • Hierarchical agents

Simple reflex agents ignore the rest of the percept history and act only on the basis of the current percept . Percept history is the history of all that an agent has perceived to date. The agent function is based on the condition-action rule . A condition-action rule is a rule that maps a state i.e., a condition to an action. If the condition is true, then the action is taken, else not. This agent function only succeeds when the environment is fully observable. For simple reflex agents operating in partially observable environments, infinite loops are often unavoidable. It may be possible to escape from infinite loops if the agent can randomize its actions. 

Problems with Simple reflex agents are : 

  • Very limited intelligence.
  • No knowledge of non-perceptual parts of the state.
  • Usually too big to generate and store.
  • If there occurs any change in the environment, then the collection of rules needs to be updated.

Simple Reflex Agents

It works by finding a rule whose condition matches the current situation. A model-based agent can handle partially observable environments by the use of a model about the world. The agent has to keep track of the internal state which is adjusted by each percept and that depends on the percept history. The current state is stored inside the agent which maintains some kind of structure describing the part of the world which cannot be seen. 

Updating the state requires information about:

  • How the world evolves independently from the agent?
  • How do the agent’s actions affect the world?

Model-Based Reflex Agents

These kinds of agents take decisions based on how far they are currently from their goal (description of desirable situations). Their every action is intended to reduce their distance from the goal. This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. The knowledge that supports its decisions is represented explicitly and can be modified, which makes these agents more flexible. They usually require search and planning. The goal-based agent’s behavior can easily be changed. 

Goal-Based Agents

The agents which are developed having their end uses as building blocks are called utility-based agents. When there are multiple possible alternatives, then to decide which one is best, utility-based agents are used. They choose actions based on a preference (utility) for each state. Sometimes achieving the desired goal is not enough. We may look for a quicker, safer, cheaper trip to reach a destination. Agent happiness should be taken into consideration. Utility describes how “happy” the agent is. Because of the uncertainty in the world, a utility agent chooses the action that maximizes the expected utility. A utility function maps a state onto a real number which describes the associated degree of happiness. 

Utility-Based Agents

A learning agent in AI is the type of agent that can learn from its past experiences or it has learning capabilities. It starts to act with basic knowledge and then is able to act and adapt automatically through learning. A learning agent has mainly four conceptual components, which are: 

  • Learning element: It is responsible for making improvements by learning from the environment.
  • Critic: The learning element takes feedback from critics which describes how well the agent is doing with respect to a fixed performance standard.
  • Performance element: It is responsible for selecting external action.
  • Problem Generator: This component is responsible for suggesting actions that will lead to new and informative experiences.

Learning Agent

Multi-Agent Systems

These agents interact with other agents to achieve a common goal. They may have to coordinate their actions and communicate with each other to achieve their objective.

A multi-agent system (MAS) is a system composed of multiple interacting agents that are designed to work together to achieve a common goal. These agents may be autonomous or semi-autonomous and are capable of perceiving their environment, making decisions, and taking action to achieve the common objective.

MAS can be used in a variety of applications, including transportation systems, robotics, and social networks. They can help improve efficiency, reduce costs, and increase flexibility in complex systems. MAS can be classified into different types based on their characteristics, such as whether the agents have the same or different goals, whether the agents are cooperative or competitive, and whether the agents are homogeneous or heterogeneous.

  • In a homogeneous MAS, all the agents have the same capabilities, goals, and behaviors. 
  • In contrast, in a heterogeneous MAS, the agents have different capabilities, goals, and behaviors. 

This can make coordination more challenging but can also lead to more flexible and robust systems.

Cooperative MAS involves agents working together to achieve a common goal, while competitive MAS involves agents working against each other to achieve their own goals. In some cases, MAS can also involve both cooperative and competitive behavior, where agents must balance their own interests with the interests of the group.

MAS can be implemented using different techniques, such as game theory , machine learning , and agent-based modeling. Game theory is used to analyze strategic interactions between agents and predict their behavior. Machine learning is used to train agents to improve their decision-making capabilities over time. Agent-based modeling is used to simulate complex systems and study the interactions between agents.

Overall, multi-agent systems are a powerful tool in artificial intelligence that can help solve complex problems and improve efficiency in a variety of applications.

Hierarchical Agents

These agents are organized into a hierarchy, with high-level agents overseeing the behavior of lower-level agents. The high-level agents provide goals and constraints, while the low-level agents carry out specific tasks. Hierarchical agents are useful in complex environments with many tasks and sub-tasks.

  • Hierarchical agents are agents that are organized into a hierarchy, with high-level agents overseeing the behavior of lower-level agents. The high-level agents provide goals and constraints, while the low-level agents carry out specific tasks. This structure allows for more efficient and organized decision-making in complex environments.
  • Hierarchical agents can be implemented in a variety of applications, including robotics, manufacturing, and transportation systems. They are particularly useful in environments where there are many tasks and sub-tasks that need to be coordinated and prioritized.
  • In a hierarchical agent system, the high-level agents are responsible for setting goals and constraints for the lower-level agents. These goals and constraints are typically based on the overall objective of the system. For example, in a manufacturing system, the high-level agents might set production targets for the lower-level agents based on customer demand.
  • The low-level agents are responsible for carrying out specific tasks to achieve the goals set by the high-level agents. These tasks may be relatively simple or more complex, depending on the specific application. For example, in a transportation system, low-level agents might be responsible for managing traffic flow at specific intersections.
  • Hierarchical agents can be organized into different levels, depending on the complexity of the system. In a simple system, there may be only two levels: high-level agents and low-level agents. In a more complex system, there may be multiple levels, with intermediate-level agents responsible for coordinating the activities of lower-level agents.
  • One advantage of hierarchical agents is that they allow for more efficient use of resources. By organizing agents into a hierarchy, it is possible to allocate tasks to the agents that are best suited to carry them out, while avoiding duplication of effort. This can lead to faster, more efficient decision-making and better overall performance of the system.

Overall, hierarchical agents are a powerful tool in artificial intelligence that can help solve complex problems and improve efficiency in a variety of applications.

Uses of Agents

Agents are used in a wide range of applications in artificial intelligence, including:

  • Robotics: Agents can be used to control robots and automate tasks in manufacturing, transportation, and other industries.
  • Smart homes and buildings: Agents can be used to control heating, lighting, and other systems in smart homes and buildings, optimizing energy use and improving comfort.
  • Transportation systems: Agents can be used to manage traffic flow, optimize routes for autonomous vehicles, and improve logistics and supply chain management.
  • Healthcare: Agents can be used to monitor patients, provide personalized treatment plans, and optimize healthcare resource allocation.
  • Finance: Agents can be used for automated trading, fraud detection, and risk management in the financial industry.
  • Games: Agents can be used to create intelligent opponents in games and simulations, providing a more challenging and realistic experience for players.
  • Natural language processing: Agents can be used for language translation, question answering, and chatbots that can communicate with users in natural language .
  • Cybersecurity: Agents can be used for intrusion detection, malware analysis, and network security.
  • Environmental monitoring: Agents can be used to monitor and manage natural resources, track climate change, and improve environmental sustainability.
  • Social media: Agents can be used to analyze social media data, identify trends and patterns, and provide personalized recommendations to users.

Overall, agents are a versatile and powerful tool in artificial intelligence that can help solve a wide range of problems in different fields.

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Search algorithms, knowledge, reasoning and planning, uncertain knowledge and reasoning, problem-solving in artificial intelligence.

The reflex agents are known as the simplest agents because they directly map states into actions. Unfortunately, these agents fail to operate in an environment where the mapping is too large to store and learn. Goal-based agent, on the other hand, considers future actions and the desired outcomes.

Here, we will discuss one type of goal-based agent known as a problem-solving agent , which uses atomic representation with no internal states visible to the problem-solving algorithms .

Problem-solving agent

The problem-solving agent perfoms precisely by defining problems and its several solutions.

According to psychology, “ a problem-solving refers to a state where we wish to reach to a definite goal from a present state or condition.”

According to computer science, a problem-solving is a part of artificial intelligence which encompasses a number of techniques such as algorithms, heuristics to solve a problem.

Therefore, a problem-solving agent is a goal-driven agent and focuses on satisfying the goal.

Steps performed by Problem-solving agent

  • Goal Formulation: It is the first and simplest step in problem-solving. It organizes the steps/sequence required to formulate one goal out of multiple goals as well as actions to achieve that goal. Goal formulation is based on the current situation and the agent's performance measure (discussed below).
  • Problem Formulation: It is the most important step of problem-solving which decides what actions should be taken to achieve the formulated goal. There are following five components involved in problem formulation:
  • Initial State: It is the starting state or initial step of the agent towards its goal.
  • Actions: It is the description of the possible actions available to the agent.
  • Transition Model: It describes what each action does.
  • Goal Test: It determines if the given state is a goal state.
  • Path cost: It assigns a numeric cost to each path that follows the goal. The problem-solving agent selects a cost function, which reflects its performance measure. Remember, an optimal solution has the lowest path cost among all the solutions.

Note: Initial state, actions , and transition model together define the state-space of the problem implicitly. State-space of a problem is a set of all states which can be reached from the initial state followed by any sequence of actions. The state-space forms a directed map or graph where nodes are the states, links between the nodes are actions, and the path is a sequence of states connected by the sequence of actions.

  • Search: It identifies all the best possible sequence of actions to reach the goal state from the current state. It takes a problem as an input and returns solution as its output.
  • Solution: It finds the best algorithm out of various algorithms, which may be proven as the best optimal solution.
  • Execution: It executes the best optimal solution from the searching algorithms to reach the goal state from the current state.

Example Problems

Basically, there are two types of problem approaches:

  • Toy Problem: It is a concise and exact description of the problem which is used by the researchers to compare the performance of algorithms.
  • Real-world Problem: It is real-world based problems which require solutions. Unlike a toy problem, it does not depend on descriptions, but we can have a general formulation of the problem.

Some Toy Problems

  • 8 Puzzle Problem: Here, we have a 3x3 matrix with movable tiles numbered from 1 to 8 with a blank space. The tile adjacent to the blank space can slide into that space. The objective is to reach a specified goal state similar to the goal state, as shown in the below figure.
  • In the figure, our task is to convert the current state into goal state by sliding digits into the blank space.

Some Toy Problems

In the above figure, our task is to convert the current(Start) state into goal state by sliding digits into the blank space.

The problem formulation is as follows:

  • States: It describes the location of each numbered tiles and the blank tile.
  • Initial State: We can start from any state as the initial state.
  • Actions: Here, actions of the blank space is defined, i.e., either left, right, up or down
  • Transition Model: It returns the resulting state as per the given state and actions.
  • Goal test: It identifies whether we have reached the correct goal-state.
  • Path cost: The path cost is the number of steps in the path where the cost of each step is 1.

Note: The 8-puzzle problem is a type of sliding-block problem which is used for testing new search algorithms in artificial intelligence .

  • 8-queens problem: The aim of this problem is to place eight queens on a chessboard in an order where no queen may attack another. A queen can attack other queens either diagonally or in same row and column.

From the following figure, we can understand the problem as well as its correct solution.

8-queens problem in Artificial Intelligence

It is noticed from the above figure that each queen is set into the chessboard in a position where no other queen is placed diagonally, in same row or column. Therefore, it is one right approach to the 8-queens problem.

For this problem, there are two main kinds of formulation:

  • Incremental formulation: It starts from an empty state where the operator augments a queen at each step.

Following steps are involved in this formulation:

  • States: Arrangement of any 0 to 8 queens on the chessboard.
  • Initial State: An empty chessboard
  • Actions: Add a queen to any empty box.
  • Transition model: Returns the chessboard with the queen added in a box.
  • Goal test: Checks whether 8-queens are placed on the chessboard without any attack.
  • Path cost: There is no need for path cost because only final states are counted.

In this formulation, there is approximately 1.8 x 10 14 possible sequence to investigate.

  • Complete-state formulation: It starts with all the 8-queens on the chessboard and moves them around, saving from the attacks.

Following steps are involved in this formulation

  • States: Arrangement of all the 8 queens one per column with no queen attacking the other queen.
  • Actions: Move the queen at the location where it is safe from the attacks.

This formulation is better than the incremental formulation as it reduces the state space from 1.8 x 10 14 to 2057 , and it is easy to find the solutions.

Some Real-world problems

  • Traveling salesperson problem(TSP): It is a touring problem where the salesman can visit each city only once. The objective is to find the shortest tour and sell-out the stuff in each city.
  • VLSI Layout problem: In this problem, millions of components and connections are positioned on a chip in order to minimize the area, circuit-delays, stray-capacitances, and maximizing the manufacturing yield.

The layout problem is split into two parts:

  • Cell layout: Here, the primitive components of the circuit are grouped into cells, each performing its specific function. Each cell has a fixed shape and size. The task is to place the cells on the chip without overlapping each other.
  • Channel routing: It finds a specific route for each wire through the gaps between the cells.
  • Protein Design: The objective is to find a sequence of amino acids which will fold into 3D protein having a property to cure some disease.

Searching for solutions

We have seen many problems. Now, there is a need to search for solutions to solve them.

In this section, we will understand how searching can be used by the agent to solve a problem.

For solving different kinds of problem, an agent makes use of different strategies to reach the goal by searching the best possible algorithms. This process of searching is known as search strategy.

Measuring problem-solving performance

Before discussing different search strategies, the performance measure of an algorithm should be measured. Consequently, There are four ways to measure the performance of an algorithm:

Completeness: It measures if the algorithm guarantees to find a solution (if any solution exist).

Optimality: It measures if the strategy searches for an optimal solution.

Time Complexity: The time taken by the algorithm to find a solution.

Space Complexity: Amount of memory required to perform a search.

The complexity of an algorithm depends on branching factor or maximum number of successors , depth of the shallowest goal node (i.e., number of steps from root to the path) and the maximum length of any path in a state space.

Search Strategies

There are two types of strategies that describe a solution for a given problem:

Uninformed Search (Blind Search)

This type of search strategy does not have any additional information about the states except the information provided in the problem definition. They can only generate the successors and distinguish a goal state from a non-goal state. These type of search does not maintain any internal state, that’s why it is also known as Blind search.

There are following types of uninformed searches:

  • Breadth-first search
  • Uniform cost search
  • Depth-first search
  • Depth-limited search
  • Iterative deepening search
  • Bidirectional search

Informed Search (Heuristic Search)

This type of search strategy contains some additional information about the states beyond the problem definition. This search uses problem-specific knowledge to find more efficient solutions. This search maintains some sort of internal states via heuristic functions (which provides hints), so it is also called heuristic search .

There are following types of informed searches:

  • Best first search (Greedy search)

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

define problem solving agent

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

define problem solving agent

  • Identify the Problem
  • Define the Problem
  • Form a Strategy
  • Organize Information
  • Allocate Resources
  • Monitor Progress
  • Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

  • Asking questions about the problem
  • Breaking the problem down into smaller pieces
  • Looking at the problem from different perspectives
  • Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

  • Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
  • Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.

Follow Now : Apple Podcasts / Spotify / Google Podcasts

You can become a better problem solving by:

  • Practicing brainstorming and coming up with multiple potential solutions to problems
  • Being open-minded and considering all possible options before making a decision
  • Breaking down problems into smaller, more manageable pieces
  • Asking for help when needed
  • Researching different problem-solving techniques and trying out new ones
  • Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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COMMENTS

  1. Problem Solving in Artificial Intelligence

    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.

  2. What is the problem-solving agent in artificial intelligence?

    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.

  3. Problem Solving Agents in Artificial Intelligence

    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.

  4. Artificial Intelligence Series: Problem Solving Agents

    Problem Solving Agents Intelligent agents are supposed to maximize its performance measure. Achieving this can be simplified if the agent can adopt a goal and aim to satisfy it. Setting goals...

  5. PDF 1.3 Problem Solving Agents Problem-solving Approach in ...

    The problem-solving agent perfoms precisely by defining problems and its several solutions. According to psychology, "a problem-solving refers to a state where we wish to reach to definite goal from a present state or condition."

  6. problemsolving

    Problem Solving Agent An agent that tries to come up with a sequence of actions that will bring the environment into a desired state. Search The process of looking for such a sequence, involving a systematic exploration of alternative actions. Searching is one of the classic areas of AI. Problems. A problem is a tuple $(S, s, A, \rho, G, P)$ where

  7. PDF Problem-Solving Agents

    solution Cal Poly SLO Goal Formulation Specify the objectives to be achieved goal a set of desirable world states in which the objectives have been achieved current / initial situation starting point for the goal formulation actions cause transitions between world states Cal Poly SLO Problem Formulation Actions and states to consider states

  8. 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.

  9. What is Problem Solving? Steps, Process & Techniques

    1. Define the problem Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes. The sections below help explain key problem-solving steps.

  10. PDF Problem Solving Agents and Uninformed Search

    Problem Solving Agent - Special type of goal based agent. Environment - static - agent assumes that in the time it takes to formulate and solve the problem the environment doesn't change observable - initial state and current state is known discrete - more than one solution possible deterministic - the solution, when executed will work!!

  11. Exploring Intelligent Agents in Artificial Intelligence

    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 ...

  12. PDF 3 SOLVING PROBLEMS BY SEARCHING

    AGENT Problem-solving agents decide what to do by finding sequences of actions that lead to desir- able states. We start by defining precisely the elements that constitute a "problem" and its "solution," and give several examples to illustrate these definitions.

  13. PDF Problem Solving and Search

    Problem Solving and Search Problem Solving • Agent knows world dynamics • World state is finite, small enough to enumerate • World is deterministic • Utility for a sequence of states is a sum over path The utility for sequences of states is a sum over the path of the utilities of the individual states.

  14. PDF Solving problems by searching

    5 Well-defined problems and solutions A problem can be defined formally by (5) components: (1) The initial state from which the agent starts. (2) A description of possible actions available to the agent: ACTIONS(s) (3) A description of what each action does, i.e. the transition model, specified by a function RESULT (s,a)=a'. Together, the initial state, actions and transition model ...

  15. Agents in Artificial Intelligence

    In artificial intelligence, an agent is a computer program or system that is designed to perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.

  16. Search Algorithms Part 1: Problem Formulation and Searching for

    How to define the problem to make it easier for finding solutions, and some basics on search algorithms in general. ... There are two kinds of goal-based agents: problem-solving agents and ...

  17. Artificial Intelligence

    00:00 - 3.1 Problem-solving agents (The Romania problem)02:10 - 3.1.1 Search problems and solutions 09:10 - 3.1.2 Formulating problems11:55 - 3.2.1 Standardi...

  18. Problem-solving in Artificial Intelligence

    The problem-solving agent selects a cost function, which reflects its performance measure. Remember, an optimal solution has the lowest path cost among all the solutions. Note: Initial state, actions, and transition model together define the state-space of the problem implicitly.

  19. What exactly do we mean by the term 'problem solving'?

    Just like creativity, problem solving is a key skill required across all sectors and at all levels. However, while the word 'creativity' can seem to refer to a very broad range of meanings, the term 'problem solving' can, in contrast, seem overly narrow. A well-defined problem can be understood, agreed upon and then solved, often using approaches that are familiar or well-practiced.

  20. Search Algorithms in AI

    Problem-solving agents are the goal-based agents and use atomic representation. In this topic, we will learn various problem-solving search algorithms. Search Algorithm Terminologies: Search: Searchingis a step by step procedure to solve a search-problem in a given search space. A search problem can have three main factors:

  21. The Problem-Solving Process

    Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything ...

  22. 6 Ways to Build Planning Agents

    Hybrid. Learning-Based Planning Agents: These agents use machine learning techniques to learn effective planning strategies from data. Reinforcement learning, imitation learning, and neural networks are often used to learn planning policies. All these implementations require adequate data for training keep in mind.

  23. Problem Solving Techniques in AI

    Simple Reflex Agents Model-Based Reflex Agents Goal-Based Agents Utility-Based Agents Learning Agents This mapping of states and actions is made easier through these agencies. These agents frequently make mistakes when moving onto the subsequent phase of a complicated issue; hence, problem-solving standardized criteria such cases.