ICS 171: Introduction to Artificial Intelligence

Fall Quarter, 2000


For Fall quarter, we will miss only one class due to holiday: Thursday, Nov 23 is Thanksgiving.

This schedule is subject to change, especially in the later parts of the quarter. Changes may appear here, but will always be also sent via email to all enrolled students. Handouts (listed in weeks 4 and 5) are available for purchase at the Engineering Copy Center. The ECC is in the Engineering Tower, on the plaza level, facing away from Aldrich Park.


Readings/Due dates


Sept 26 & 28
Introduction and Background

Text, chapter 1 (and optionally, chpt. 26)

What is artificial intelligence? What are some modern examples of AI? Related fields: philosophy, psychology, mathematics, computer engineering, etc. AI from a rational agent perspective. Agent architectures and programs.

Oct 3 & 5
Problem-Solving by Search

Text, chpts 3 & 4. Skip end of section 4.1 (proofs of A* behavior). Skip end of section 4.3, the SMA* algorithm.
(You must know and understand material in Appendix A)
Handouts: team project questionnaire

Principles of Search: Goal and problem formulation. Types of search problems. Abstraction.
Uninformed ("Blind") Search: Breadth-first, depth-first, uniform-cost, depth-limited, iterative-deepening, and bidirectional search techniques. Constraint satisfation problems. Time-space complexity. Completeness and optimality.
Informed ("Heuristic") Search: Best-first, A*, search techniques. Heuristic functions. Search and optimization. Hill-climbing techniques.

Oct 10 & 12
More Search

Text, chpt 5.1 - 5.4.
QUIZ #1: Search, Oct 12th.
Handout: First programming assignment: Gomoku game playing program. Due Oct 26.

Advanced search methods: Iterative deepening A* (IDA*), constraint satisfaction methods, Genetic algs? Simulated annealing? (Time permitting)
Game playing search: MiniMax search & the Alpha-Beta cutoff algorithm.

Oct 17 & 19
Knowledge representation, logic and inference.

Handout: Luger and Stubblefield: pp. 47-67, & 75-78.
Text, chpt 9, pp. 265 - 277
(Optional: text, chpt 7.1 - 7.3)

Logic: Propositional calculus, predicate calculus. Inference: Modus ponens, unification, forward chaining, backward chaining.
Knowledge representation (KR) issues and problems.

Oct 24 & 26
Expert systems (knowledge-based systems) and planning systems.

Handout: Luger & Stubblefield: pp. 207 - 231.
Handout: Programming assignment #2: CLIPS, due Nov 9
Programming assignment #1, due Oct 26

Building knowledge-based systems (expert systems). Expert system shells. Discussion of the CLIPS expert system tool.
Linear  planners (the STRIPS system); partial-order planners


Oct 31 & Nov 2
Uncertainty, Probability and Decision making

Text, chpt 11. Skip sections 11.6 and 11.7. QUIZ #2: KR, logic & planning, Oct 31.
Text, chapter 14.1 (only)

More on planning. Introduction to Uncertainty, Probability and Decision making.

Nov 7 & 9
Machine Learning

Text, chpt 18.1 - 8.3 (thru p. 540) and chpt 19.1 - 19.3. (optionally, 19.4 & 19.5)
Programming assignment #2, due Nov 9
Handout: Programming assignment #3: Perceptron learning, due Dec 1.

Deductive vs. Inductive learning, prior knowledge, performance estimation. Decision trees.
Classification, clustering, reinforcement learning, perceptrons and neural networks. (Guest lecture on Nov 7.)

Nov 14 & 16
Natural Language Processing

Text, chpt 22, only thru section 22.4

Finish Machine learning; Introduction to Natural language processing; syntactic parsing

Nov 21
(Nov 23 is Thanksgiving)

Text, chpt 23, except section 23.2

Speech Understanding, NL generation, NL understanding, NL Parsing. Message understanding, discourse understanding, ambiguity. Grammars for NLP.

Nov 28 & 30

QUIZ #3: Machine learning & NLP, Nov 28.
Programming assignment #3, due Dec 1
Text, chpt 26.

Overview of AI; Review for final exam

Dec 7 Final Exam, Dec 7, 1:30pm - 3:30pm.