Objectives:
The realm of Artificial Intelligence, its applications. Knowledge representation methods for problem solving. Generic problem solving methods. Search methods, heuristic search, and optimal search. Rules, rule chaining, logic, and resolution proof. Planning. Learning methods and neural networks. Vision and natural language understanding and processing.
Prerequisites: CS 361, recommended: CS 303 and CS 304.
Textbook:
Patrick H. Winston Artificial Intelligence 3rd ed. Addison Wesley, 1992
References: Journals and conference proceedings in Artificial Intelligence
Outline:
1. Overview: AI defined, its applications, and criteria for judging success. 2. Semantic nets and problem solving. 3. Some generic problem solving methods: generate and test, means-ends analysis, and problem reduction. 4. Search methods for problem solving: basic, heuristic, and optimal search methods, game tree methods. 5. Knowledge representation and problem solving with rules, explanations. 6. Knowledge representation and problem solving with frames, common sense representation. 7. Conclusion propagation. 8. Logic for knowledge representation and resolution theorem proving, truth maintenance. 9. Goal achieving with planning. 10. Learning with reasoning, database mining. 11. Neural networks. 12. Understanding vision and natural language.