Week 1: Introduction to AI Search
Key Topics:
- Definition and Importance of Search in AI
- Search is a fundamental problem-solving tool in AI, modeling problems as states, actions, and transitions.
- Basic Terminology
- State Space: Represents all possible configurations of the problem.
- Initial State: Starting point of the problem.
- Goal State: Desired solution or end configuration.
- Action: Moves between states.
- Path Cost: Total cost of transitions.
- Uninformed Search Techniques
- Methods that explore the search space without domain-specific knowledge.
- Examples:
- Breadth-First Search (BFS): Explores all nodes at the current depth level.
- Depth-First Search (DFS): Explores as far as possible along a branch, then backtracks.
Week 2: Foundational Search Techniques
Key Topics:
- Informed Search
- Heuristics: Functions that estimate the cost to reach the goal.
- Example: In a maze, the straight-line distance to the exit is a heuristic.
- Comparison of BFS and DFS
- BFS ensures the shortest path but requires more memory.
- DFS is memory-efficient but may not find the optimal solution.
Week 3: Heuristic Search and Escaping Local Optima
Key Topics:
- Heuristic Search
- Uses heuristics to guide exploration.
- Hill Climbing: Selects the neighbor state with the highest value.
- Limitation: May get stuck in local optima, plateaus, or ridges.
- Escaping Local Optima
- Simulated Annealing: Occasionally accepts worse solutions with a decreasing probability over time.
Week 4: Stochastic Local Search (SLS)
Key Topics:
- Stochastic Search
- Introduces randomness in the exploration process.
- Examples:
- Random Restart: Repeatedly starts the search from random points.
- Genetic Algorithms: Inspired by evolution, uses crossover and mutation.