University of Washington
Department of Computer Science and Engineering

 

CSE573–Artificial Intelligence I

Autumn 1997

MWF 12:30--1:20, Sieg 224

 

 

Professor

TA

Steve Hanks

Michael Noth

210 Sieg, 543-4784

423 Sieg

hanks@cs.washington.edu

noth@cs.washington.edu

 

Course home page:

http://www.cs.washington.edu/education/courses/573/CurrentQtr

Course mailing list:

cse573@cs.washington.edu

 

 

Texts

 

Evaluation and Assignments

 

 

Tentative List of Topics

 

Topic

Lects

Reading

Introduction and overview

  • AI, the text, course perspective
  • LISP review, building a simple agent

3

Chapters 1 and 2

Search

  • The state-space search paradigm
  • Uninformed and informed approaches

2

Chapters 3 and 4

Logic and deduction

  • Logic’s role in AI
  • Syntax and semantics
  • Time, change, sets, arithmetic.
  • Automated deduction: forward and backward chaining, resolution

4

Chapters 6, 7, 9, 10

Planning

  • The basic planning problem and representation.
  • Planning algorithms: total order, partial order, decomposition, transformation.
  • Extended representations: conditional effects, quantification, resources.
  • Planning and execution: conditionals, replanning.
  • Planning and uncertainty: incomplete information, faulty sensors and effectors.

9

Chapters 11, 12, 13 Several UW papers.

Uncertainty and decision making

  • The role of probability theory in AI.
  • Probabilistic networks and inference.
  • Basic decision theory and influence diagrams.
  • Value of information.
  • Complex decision making.

6

Chapters 15, 16, 18 Several UW papers.

Learning

  • Inductive learning
  • Reinforcement learning
  • Knowledge-based learning (EBL)

6

Chapter 18, 20, 21