Index of class resources

Handouts — homework problem sets, homework solutions, other helpful handouts
General Class Information — class and section times, instructor and TA information

• Course Syllabus
• Resources and Guidelines
• Homework

Required:

AI Text: "Artificial Intelligence: A Modern Approach"
Stuart Russell and Peter Norvig
Prentice Hall, Second Edition. 2003 ISBN 0-13-790395-2

ET: "Probability Theory: Logic of Science"
Jaynes, E.T. and G. Larry Brethorstt
Cambridge Press 2003

CH: the UCSC Reader by Chris Harrison:
"Determining Satisfiability Through Subtraction And a Binary
Constrained XOR Form (BXCF)."

References:

"Algebra of Probable Inference"
Richard T. Cox
1961 John Hopkins Press

"Artificial Intelligence., Second Edition!"
Rich, Elaine. Knight, Kevin
McGraw-Hill Book Co., New York, 1990

"Artificial Intelligence, 3rd edition"
Winston, P.H.
Addison-Wesley, 1992.

"Elementary Probability"
David Stirzaker
Cambridge University Press, 1994.

"An Introduction to Natural Computation"
Dana Ballard
The MIT Press, 1997.

Prerequisites:

Grad. Standing, or instructor's permission. Previous AI course is helpful but not required.

(CMP140 = approximately chapters 1-5, 13, 18, 20 of above but at a somewhat lower level).

Project Help:

Peirce's Existential Graphs for Propositional Logic: http://plato.stanford.edu/entries/peirce-logic/#EG

OTTER Theorem Proving Page - includes benchmark problems (TPTP): http://www-unix.mcs.anl.gov/AR/otter/

Satisfiability Benchmarks and Solvers! http://www.satlib.org/index-ubc.html

Tile Puzzles: A Pattern-Weight Formulation of Search Knowledge by Gil Fuchs and Robert Levinson

Some interesting web pages on intelligent agents: http://www.cs.umbc.edu/agents/ http://www.cs.berkeley.edu/~russell/ai.html

Internet Chess Club: telnet chessclub.com or http://www.chessclub.com

Interesting Papers on Complex Adaptive Systems: http://www.santafe.edu/research/publications/working-papers.php

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Course Cheer: CASTLE EARLY and OFTEN.

Course Equation: Total Information = Diversity (Complexity) + Symmetry In Parts

Course Word: SUBSUMPTION

Course Symbol: *

During the course there will be a multi-stage programming project, regular homework problems and a final exam. The development of a Subtraction-Based Boolean Satisfier (see CH), FirstOrder--> SAT theorem prover, 8 puzzle ---> Factors solver. Random Number prediction and Morph (adaptive chess) are likely some of the projects.

Course Evaluation:

• Programming Project %40
• Written Homework %40
• Final Exam %10
• Class Participation %10

The written homeworks are to be done individually. Programming projects may be done in teams of 2-3 that are setup at the beginning of the quarter and maintained throughout the quarter. All work will be graded competitively as well as qualitatively.

It is fine and encouraged to discuss homework problems with other students - BUT CHEATING or ACADEMIC DISHONESTY on any course item (such as direct verbatim copying from a member outside your group or during an exam) will result in not passing the course and other undesirable consequences. Written problem solutions should reflect your own understanding and be in your own words.

Exceptional performance will be recognized (extra credit).

Both the Final and Project Assignments must be completed at a satisfactory level to pass the course. Superior performance on the final can make up for weaknesses elsewhere.

Lecture Schedule (tentative)

Problems below are assigned unless otherwise announced in class or in the newsgroup. Other problems may be added in addition to these.

In addition to the text (AI Text:), chapters are assigned from the book, "Probability Theory: The Logic of Science" (ET:). CH: refers to the Harrison reader.

EC= not required, extra credit.

Due dates for project to be announced. There will be a series of project checkpoints.

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I. Introduction to Expert Systems

Text: Chapters 1-3 are mainly review. 6-7.

HW0: handout in class. due thursday april 6.

HW1: due thursday april 13.

AI Text: Exercises 6.1, 7.4, 7.5, 7.6, 7.8, 7.9
ET: Chapter 10. No exercises.
CH: Chapter 1-2 Write an exercise for each chapter and answer it.

1. (Tues. April 4)

2. (Thurs April 6) HW0 is due. [see handout]

II. Knowledge Bases and Inference

HW2: Due: Thursday April 27
AI Text: Chapters 8-10: 8.2, 8.6adgjk, 9.3, 9.9, 9.11, 9.13, 9.14, 9.18
ET: Chapters 1-2: 2.1-2.3
CH: Chapters 3-4, Write an exercise for each chapter and answer it.

3. (Tues. April 11)

4. (Thurs. April 13) HW1 is due.

5. (Tues. April 18)

6. (Thurs. April 20) Project checkpoint1 is due.

III. Reasoning Under Uncertainty

HW3: Due Thur, May 11:
AI Text: Chapters 14-16 14.3, 16.3, 16.7, 16.11
ET: Chapter 3: 3.1-3.5
CH: Chapters 5-6 Write an exercise for each chapter and answer it.

7. (Tues. April 25)

8. (Thurs. April 27) HW2 is due

9. (Tues. May 2)

12. (Thurs. May 4) Project checkpoint2 is due.

IV. Pattern Matching, Analogy and Retrieval

HW4: Due Thursday May 25 (postponed to July 6)
AI Text: Papers and problems to be handed out in class.
ET: Chapter 4: 4.1-4.4
CH: Chapters 7-8 Write an exercise for each chapter and answer it.
Due: Thursday May 19

13. (Tues. May 9)

14. (Thurs. May 11) HW3 is due

15. (Tues. May 16)

16. (Thurs. May 18) Project checkpoint 3 is due.

17. (Tues. May 23)

18. (Thurs. May 25) HW4 is due (postponed to 6/6)

19. (Tues. May 30)

20. (Thurs. June 1) Project Checkpoint 4 is due.

21. (Tuesday June 6) HW4 new due date

22. (Thursday June 8) Projects are Due. Final Exam handed out.

23. (Tuesday June 13) Final Exam Due at 3pm.

The final exam will come from material in the lectures, projects and textbook. The exam will be cumulative. It could very well be a takehome exam involving critical reviews of research papers in the field or other.

Class Newsgroup:

ucsc.class.cmp240 - for announcements, general discussion, and help

Optionally Follow:

comp.ai.alife

Lecture times:

TuTh, 12-1:45 pm, College 8, Classroom 242

Additional Papers:

A Pattern-Weight Formulation of Search Knowledge (ps)

The CG Mars Lander (ps)

Instructor:

Name: Robert Levinson (levinson@soe.ucsc.edu)

Phone: 459-2087 (x2087 on-campus)

Office: E2 255

Instructor Office Hours:

Wednesday 2-6 pm, after class or by appointment

Reader:

Name: David Ilstrup (dmilstru@ucsc.edu)