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
Undergraduate Pre or Corequisite
none
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
1. Student will be able to explain a variety of heuristic search strategies and the advantages/disadvantages of each. 2. Student will understand and use central concepts of knowledge representation including production rules, frames and scripts, and semantic networks. 3. Student will understand and use central concepts of problem solving, planning and control including constraints, forward and backward chaining, and solution generation and evaluation. 4. Student will understand basic techniques of automated theorem proving and be able to explain their use in classic AI programs and languages. 5. Student will be able to explain expert systems and natural language processing in terms of the fundamental concepts of AI research they involve. 6. Student will write simple programs in LISP, demonstrating a knowledge of LISP syntax and data structures. 7. Student will read classic LISP programs and explain their operation. 8. Student will demonstrate familiarity with classic criticisms of artificial intelligence research.
Topical Outline
- Heuristic Search - Knowledge Representation - Problem Solving - Planning - Control - Automated Theorem Proving - Expert Systems - Natural Language Processing - Cognition as Computation - Computer Vision
Syllabus