09_Problem_Solving_Agents
Transition Model & Problem Solving Agents
Transition Model
A Transition Model describes how the environment changes in response to the agent's actions. It specifies the result of applying an action in a particular state.
Key Aspects:
- Defines state transitions
- Maps (state, action) pairs to resulting states
- May be deterministic or stochastic
- Essential for planning and search
Problem Solving Agent
Definition
A Problem Solving Agent is an agent that plans ahead by considering a sequence of actions that form a path to a goal state when the correct action to take is not immediately obvious.
Characteristics
- Uses search as the computational process
- Considers multiple possible action sequences
- Evaluates paths before execution
- Goal-directed behavior
When to Use Problem Solving Agents
- Environment is predictable
- Actions have known effects
- Goal is clearly defined
- Solution requires multiple steps
Search Process
Search is the computational process undertaken by problem-solving agents to find a sequence of actions leading from the initial state to a goal state.
Search Components:
- State Space: All possible configurations
- Initial State: Starting point
- Goal Test: Determines if goal is reached
- Actions: Available choices at each state
- Transition Model: Effects of actions
- Path Cost: Total cost of action sequence
Example: Route Finding
- States: Cities on a map
- Initial State: Starting city
- Goal: Destination city
- Actions: Drive between connected cities
- Transition Model: Roads connecting cities
- Path Cost: Distance traveled
Types of Search Problems
1. Toy Problems
- Simple, well-defined
- Used for testing algorithms
- Examples: 8-puzzle, Missionaries and Cannibals
2. Real-world Problems
- Complex, practical applications
- Examples: Route planning, scheduling, robotics
Search Strategy
A search strategy determines which state to expand next during the search process.
Key Decisions:
- Order of node expansion
- When to stop searching
- How to handle repeated states
- Memory vs. time trade-offs
Goal: Find a solution efficiently while minimizing computational cost.
Means-End Analysis (MEA)
Means-End Analysis is a problem-solving strategy that aims to reduce the difference between the current state and the goal state. It was famously implemented in the General Problem Solver (GPS).
How it Works:
- Identify Differences: Compare current state S_i with goal state S_g.
- Select Operator: Choose an operator O that can reduce the most significant difference.
- Apply Operator:
- If preconditions are met, apply O.
- If not, set a subgoal to satisfy the preconditions of O.
- Recurse: Apply MEA to solve subgoals.
General Problem Solver (GPS)
- Developed by Newell, Simon, and Shaw (1950s).
- First AI system to separate task knowledge (objects/operators) from the search strategy.
- Uses a Difference Table to map differences to operators.