University of Washington

University of Washington
Department of Computer Science and Engineering

CSE473–Artificial Intelligence I

Winter 1998

MWF 2:30--3:20, EEB 108

Professor	TA
Steve Hanks	Steven Wolfman
210 Sieg, 543-4784	428 Sieg, 685-2723 (Office Hrs: 326 Sieg)
hanks@cs.washington.edu	wolf@cs.washington.edu

Course home page:	http://www.cs.washington.edu/education/courses/473/
Course mailing list:	cse473@cs.washington.edu

Texts

Required: Dean et al., Artificial Intelligence: Theory and Practice

Recommended: a Lisp Programming Book if you need one to refresh your memory about Lisp programming. The course's web page has a list of suggested books and pointers for on-line ordering.
Several papers to be distributed in class.

Evaluation and Assignments

Five written/programming assignments, four of which will be handed in and graded. (12.5% each)
Homework late policy. You have a total of four late days to use however you want. After that, no credit, no exceptions.
Midterm exam (20%). Date for midterm: Friday, February 13.
Final exam (30%). Date for final: Tuesday, March 17.

Tentative List of Topics

Topic	Lectures	Reading
Introduction and overview AI, the text, course perspective LISP review, building a simple agent	1	Chapter 1
LISP programming, pattern matching, reactive agents LISP programming Building and understanding LISP systems Application: building a reactive agent based on pattern matching and macro-operators	5	Chapter 2
Search The state-space search paradigm Uninformed and informed approaches Optimizing and satisficing	5	Chapter 4
Planning and acting The basic planning problem and representation Planning algorithms: total order, partial order, decomposition. Extended representations: conditional effects, quantification, resources Planning and execution: conditionals, re-planning	5	Chapter 7, and several additional papers.
Natural language processing Syntactic parsing Semantic analysis Conceptual dependency Discourse and episodes	5	Chapter 10
Uncertainty and decision making The role of probability theory in AI Probabilistic networks and inference Basic decision theory and influence diagrams	5	Chapter 8