The document discusses the approach to problem solving using artificial intelligence (AI), emphasizing the need for a formal problem description and the use of AI components such as knowledge representation and heuristic search techniques. It outlines the steps involved in identifying and formulating problems, along with the role of goal-based agents in the problem-solving process. Key components include defining initial states, available actions, goal tests, and path cost functions.

1. PROBLEM SOLVING USING AI
Artificial Intelligence

2. PROBLEM SOLVING APPROACH TO
TYPICAL AI PROBLEMS
Problems are the issues which comes across any system
A solution is needed to solve any problem
Classical approach is hit and trial method (Generate and test)
AI components required to solve problem
Knowledge Representation
Heuristic Searching Techniques
AI Hardware
Computer Vision and Pattern Recognition
NLP
AI Language Support and tools

3. PROBLEM DEFINING AND SOLVING
PROBLEM
 Problem Solving
 The process of identifying a problem, developing possible
solution paths, and taking the appropriate course of action.
 A formal description of the problem is required
 Aspects of the problem
 Explicit goal of the problem
 Implicit criteria for Success
 Initial Situation
 Ability to perform

4. WELL DEFINED PROBLEMS
 Problem Formulation
 The process of deciding what actions and states to consider given a
goal
 Four components
 Initial state that the agent starts in
 A description of the possible actions available to the agent
 The goal test
 A path cost function (performance measure)
 Problem Formulation Types
 Incremental formulation (starting with an empty state)
 Complete-state formulation (starting with some basic configuration)

5. SOLVING THE PROBLEM
Problem Definition
Problem Analysis
Knowledge Representation
Problem Solving

6. PROBLEM SOLVING AGENTS
 Goal based agents are called Problem solving agents
 Steps in Problem Solving
 Goal Setting
 Goal Formulation
 To observe current state
 To tabulate agents performance measures
 Problem Formulation
 Search in unknown environment
 Execution Phase
Problem
Solving Agent
Sequence of
Actions
Successful
states
Generate Act