91¶ÌÊÓƵ

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