CS 270 ARTIFICIAL INTELLIGENCE (3) II
Lecture: 3 hours
Prerequisite: Courses ECS 140A, ECS 170
Grading: Letter; exercise sets (50%), final project (50%).
Concepts and techniques underlying the design and implementation of models of human performance on intelligent tasks. Representation of high-level knowledge structures. Models of memory and inference. Natural language and story understanding. Common sense planning and problem solving.
Provide the conceptual models and algorithmic tools to build programs to accomplish intelligent tasks, particularly natural language understanding and common sense problem solving. Prepare students to understand and conduct research in artificial intelligence.
Expanded Course Description:
- Knowledge Representation
- Predicate calculus
- Semantic networks
- Case frames
- Conceptual dependency
- Models of Memory and Inference
- Inference rules, forward and backward
- Plans and goals
- Natural Language and Story Understanding
- Single sentence parsing
- Expectation-based parsing
- Context-guided story understanding
- Common sense Planning and Problem Solving
- Models of memory for planning
- Commonsense planning
- Goal detection
- Selected Topics
- Speech acts
- Knowledge representation languages
S.J. Alvarado, Understanding Editorial Text: A Computer Model of Argument Comprehension, Kluwer Academic Publishers, 1990.
M.G. Dyer, In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension, MIT Press, 1983.
R.C. Schank, Dynamic Memory: A Theory of Reminding and Learning in Computers and People, Cambridge University Press, 1982.
S. Slade, The T Programming Language: A Dialect of LISP, Prentice-Hall, 1987.
Instructor: The Instructional Staff
Prepared by: A. Prieditis (Dec. 1992)
THIS COURSE DOES NOT DUPLICATE ANY EXISTING COURSE