CPSC 420 Midterm Review: Artificial Intelligence and Search Algorithms - Prof. Y. Choe, Study notes of Computer Science

Download CPSC 420 Midterm Review: Artificial Intelligence and Search Algorithms - Prof. Y. Choe and more Study notes Computer Science in PDF only on Docsity! CPSC 420 Midterm Review: Overview • AI basics • Search as a problem solving strategy • Game playing • Propositional logic • No Lisp questions. 1 AI Basics • Disciplines with ties to AI: think about how they did and would contribute • What are the hard problems in AI? Why are they hard? • Just read over the slides so that you have the general idea. 2 Uninformed Search • Description of a search problem: initial state, goals, operators, etc. • Considerations in designing a representation for a state • Evaluation criteria • BFS, DFS: time and space complexity, completeness • When to use one vs. another • Node visit orders for each strategy • Tracking the stack or queue at any moment 3 Uninformed Search / Informed Search • DLS, IDS, BDS search order, expansions, and queueing • DLS, IDS, BDS evaluation • DLS, IDS, BDS: suitable domains • Repeated states: why removing them is important • Constraint Satisfaction Search: what kind of domains? why important? 4 Informed Search • Best-first-search: definition • Heuristic function h(n): what it is • Greedy search: relation to h(n) and evaluation. How it is different from DFS (time complexity, space complexity). • Difference between heuristic search (or hill-climbing) and greedy search. • A∗: definition, evaluation, conditions of optimality • Complexity of A∗: relation to error in heuristics • Designing good (admissible) heuristics: several rule-of-thumbs 5 Informed Search: Iterative Improvement Algorithms • IDA∗: evaluation, time and space complexity (worst case) • What is a dominant heuristic and why is it better? • Hill-climbing basics and strategies • Beam search concept • Simulated annealing details: core algorithm, effect of T and ∆E, source of randomness. 6 Game Playing • Game playing: what are the types of games? • Minimax: definition, and how to get minmax values • Minimax: evaluation 7 α− β Pruning • α− β pruning: the algorithm, rationale, and why it saves time • α− β pruning algorithm: know how to apply pruning • α− β pruning properties: evaluation • Games with an element of chance: what are the added elements? how does the minmax tree get augmented? 8