Course Description
The artificial intelligence approach to modeling cognitive processes. Topics include an introduction to heuristic methods, problem representation and search methods, classic AI techniques, and a review of the controversial issues of the AI paradigm of cognition as computation.
Additional Requirements for Graduate Students:
Graduate students will work on advanced project oriented research assignments that focus on a particular aspect of AI, and then report on their findings.
Athena Title
ARTIF INTELLIGENCE
Prerequisite
CSCI(MATH) 2610 or PHIL 2500
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
Students will be familiar with standard blind and heuristic search techniques used in artificial intelligence applications and with their computational complexity. Students will be familiar with standard methods of knowledge representation used in artificial intelligence. Students will be familiar with the most common philosophical issues raised by artificial intelligence research and with the most common philosophical positions taken on these issues. Students will also be expected to be familiar with several of the standard methods associated with topics listed below, but students will not be expected to achieve familiarity with all of these methods in any single offering of the course.
Topical Outline
Several of the following topics will be included in any offering of the course. What is artificial intelligence? Exhaustive search methods Heuristic search methods Computational complexity of search methods Representing knowledge using logic Representing knowledge using frames Representing knowledge using semantic nets Uncertainty and Bayes' Theorem Uncertainty and certainty factors Uncertainty and nonmonotonic reasoning Partially ordered planning Hierarchical planning Learning by rule induction Learning using neural nets Parsing and natural language processing Pattern recognition Computer vision Robotics Can machines think?
Syllabus