Week 1: Introduction to AI Search

Key Topics:

1. Definition and Importance of Search in AI
   • Search is a fundamental problem-solving tool in AI, modeling problems as states, actions, and transitions.
2. 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.
3. 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:

1. 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.
2. 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:

1. 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.
2. Escaping Local Optima
   • Simulated Annealing: Occasionally accepts worse solutions with a decreasing probability over time.

Week 4: Stochastic Local Search (SLS)

Key Topics:

1. 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.