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This course is a survey of knowledge-based artificial intelligence. Topics include the history, definition, philosophical foundations, search techniques, game playing, propositional logic, predicate logic, knowledge representation, planning, and the natural language processing and agents.
Course Learning Outcomes:
1) Students shall be able to understand the fundamental issues in artificial intelligence including definitions, philosophical questions, modeling the world, and the role of heuristics.
2) Students shall be able to understand problem spaces, brute-force search, best-first search, two-player games, and constraint satisfaction.
3) Students shall be able to represent knowledge using propositional and predicate logic.
4) Students shall be able to apply reasoning in algorithms including resolution and theorem proving, non-monotonic inference, probabilistic reasoning, and Bayes theorem.
5) Students shall be able to apply advanced search techniques including genetic algorithms, simulated annealing, and local search.
3.000 Credit hours
3.000 Lecture hours
Levels: Undergraduate
Schedule Types: Lecture, Tutorial
Computer Science & Mathematics Department
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