Artificial Intelligence: Search and Reasoning

Formulate real-world problems as state-space search problems.

Implement the A* search algorithm to find the optimal path in a weighted graph.

Formulate problems as Constraint Satisfaction Problems (CSPs).

Apply the backtracking algorithm to solve Constraint Satisfaction Problems.

Implement the Minimax algorithm for adversarial, two-player games.

Model sequential decision-making problems using Markov Decision Processes (MDPs).

Explain core reinforcement learning concepts: states, actions, rewards, and policies.

Distinguish between value-based and policy-based reinforcement learning methods.