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