CS 4810 ARTIFICIAL INTELLIGENCE (4) 2005 Catalog Description: "Intelligent" computer programs and models of human intelligence. Agents, game playing, robotics, computer vision, understanding natural language, knowledge engineering, computer learning. Prerequisite: CS 3240 Course Description: Fundamental issues - History of artificial intelligence - Philosophical questions: Turing test, Searle's "Chinese Room" thought experiment, ethical issues in AI - Fundamental definitions: Optimal vs. human-like reasoning, Optimal vs. human-like behavior - Philosophical questions - Modeling the world - Rational agents - The role of heuristics AI as Search - State spaces - Brute-force search (breadth-first, depth-first, depth-limited, iterative deepening, bidirectional, uniform cost) - Heuristic search (generic best-first, A*, admissibility of A*) - Two-player games (minimax search, alpha-beta pruning) - Constraint satisfaction (backtracking and local search methods) - Stochastic methods: evolutionary algorithms and simulated annealing AI as Knowledge representation and reasoning - Review of propositional and predicate logic - Resolution and theorem proving - Nonmonotonic inference: Productions systems and expert systems - Finite probability space, probability measure, events - Conditional probability, independence, Bayes' theorem - Structured representation: Frames, scripts, inheritance - Integer random variables, expectation - Uncertainty: Probabilistic reasoning, Bayesian nets, Fuzzy sets and possibility theory, Decision theory AI as Machine Learning - Symbol-based - Connectionist (threshold logics, perceptrons, neural networks) - Social and Emergent Recommended Texts: Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Ed. Russell & Norvig: Artificial Intelligence: A Modern Approach