COMP338: Artificial Intelligence
Lecture Material
A few selected lecture notes are provided here to support the concepts covered in class. For the full list of lecture notes, please visit Jarrar's AI course page.
Chapter 3: Informed Search
For full details and worked exercises (Romania map, coordinate exercise, 8-puzzle heuristics, pruning maze, A* complexity in depth, RBFS and SMA*), see the Search Companion, Parts Eight and Nine.
Chapter 4: Local Search
For the full treatment with worked examples (8-queens annotated board for hill climbing, simulated annealing with numeric acceptance-probability walkthrough, a five-card genetic algorithm worked example on 8-queens, stopping-criteria summary), see the Search Companion, Part Ten.
Study Companions
Problem Formulation and Search Algorithms
An interactive walkthrough of how AI problems are formulated as search: states, actions, goal tests, and path costs, followed by uninformed and informed search algorithms with worked examples. Covers chapters 2 to 4 of the course (problem-solving agents, uninformed search, informed search, local search) plus memory-bounded heuristic search.
Open companion