In this article A Problem-Solving Agent in Artificial Intelligence is an intelligent system that defines problems, searches for solutions, and selects optimal actions to achieve goals. Learn about its structure, working process, types, and real-life applications like route planning and robotics.
Problem-Solving Agents in Artificial Intelligence
Introduction
In Artificial Intelligence (AI), a Problem-Solving Agent is an intelligent agent that analyzes problems, identifies possible actions, and selects the best solution to achieve a given goal.
This agent acts rationally to solve any problem — that is, it always chooses the action that maximizes the likelihood of reaching the goal.
What Does a Problem-Solving Agent Do?
The main tasks of a problem-solving agent are:
- Define the problem
- Identify possible actions
- Search for a solution
- Select the best solution
- Execute the chosen action
The Problem-Solving Process
A problem-solving agent works through the following steps:
1. Goal Formulation
The agent first decides what it wants to achieve.
Example: “Find gold” or “Reach a destination.”
2. Problem Formulation
The agent determines how to move from the current state to the goal state.
This involves defining:
- Initial State – Where the agent starts
- Possible Actions – The moves available to the agent
- Results of Actions – The outcome of each possible action
- Goal State – The desired outcome
- Path Cost – The cost of moving from one state to another
3. Search
The agent tries different actions to find a path that leads to the goal.
This step is carried out using search algorithms such as Breadth-First Search (BFS), Depth-First Search (DFS), or A Algorithm*.
4. Action
Once the agent finds a suitable path to the goal, it executes the actions along that path.
5. Execution and Learning
The agent observes the results of its actions and learns from experience to make better decisions in the future.
Structure of a Problem-Solving Agent
A problem-solving agent is also known as a goal-based agent.
Its functioning depends on the following components:
- Initial State: The starting point of the agent.
- Actions: The possible moves or operations the agent can perform.
- Transition Model: Defines the new state that results from a particular action.
- Goal Test: Determines whether the current state is the goal state.
- Path Cost: Measures the efficiency of the path chosen.
Types of Problem-Solving Agents
- Single-State Problem Agent
The agent has complete knowledge of the environment and the current state.
Example: Tic-Tac-Toe game. - Multiple-State Problem Agent
The agent does not have complete knowledge and works based on probabilities.
Example: Robot Navigation. - Contingency Problem Agent
The agent is prepared to handle unexpected situations.
Example: A self-driving car adapting to traffic changes. - Exploration Problem Agent
The agent explores an unknown environment to gather knowledge.
Example: NASA’s Mars Rover.
Example: Route-Finding Problem
Suppose an agent needs to travel from Pune to Mumbai.
- Initial State: Pune
- Goal State: Mumbai
- Actions: Different routes (e.g., NH48, Expressway)
- Transition Model: Distance and travel time for each route
- Path Cost: Time or distance
- Solution: Choosing the shortest and safest route.
Thus, the agent makes the best possible decision using the available information.
Characteristics of Problem-Solving Agents
- Rational decision-making ability
- Goal-oriented behavior
- Search and planning capability
- Learning from experience and the environment
Applications of Problem-Solving Agents
- Route Planning
- Game Playing
- Robotics
- Medical Diagnosis
- Machine Learning and Expert Systems
Conclusion
Problem-Solving Agents are among the most essential components of Artificial Intelligence.
They analyze, plan, and act logically to find solutions even in complex situations.
Hence, a Problem-Solving Agent serves as a model of how an intelligent machine can think, learn, and act to achieve a specific goal efficiently.
POP- Introduction to Programming Using ‘C’
OOP – Object Oriented Programming
DBMS – Database Management System
RDBMS – Relational Database Management System
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