This course introduces the historical and philosophical foundations of artificial intelligence, along with general problem-solving frameworks and key search algorithms, including domain-independent, stochastic, local, and population-based methods. It also covers problem decomposition, rule-based approaches, game-playing algorithms, and the links between search, planning, constraints, and logical reasoning.

Lectures-Tutorials-Practicals-Credits: 4-0-0-4

Week 1: Introduction; Machine learning evolution; Cognitive architecture; History and philosophy; Minds and machines; Problem solving.

Week 2: State space search; Search algorithms; Planning problems; Blind search; DFS, BFS, and iterative deepening.

Week 3: Heuristic search; Hill climbing; Local search methods; Solution space search.

Week 4: Hill climbing variants; Stochastic search; Genetic algorithms; TSP with GAs; Ant colony optimization.

Week 5: TSP optimization; Branch and bound; A* search; Admissibility of A*.

Week 6: A* variants; IDA* and RBFS; Monotone condition; Sequence alignment; Pruning in A*.

Week 7: Game theory; Game types and trees; Evaluation functions; Minimax and Alpha-Beta; SSS* algorithm; Game demos.

Week 8: Automated planning; Blocks world; State space planning; Goal stack planning; Partial order planning.

Week 9: Robot planning; Means-ends analysis; Graphplan; Problem decomposition; AO* and goal trees.

Week 10: Expert systems; Match, resolve, and execute; Rete algorithm and networks.

Week 11: CSPs; Constraint solving; Model-based diagnosis; Arc consistency; Constraint propagation.

Week 12: Lookahead search; Course summary.