CIS 241: Knowledge Engineering
Winter Quarter, 1999
Lectures: MWF 930-1040am Social Sciences II - 137.
Instructor: Robert Levinson
Office Hours: MWF 11-noon or by appointment.
Phone number: 459-2087 (x2087 on campus)
E-mail: levinson@cis.ucsc.edu (use it!)
Newsgroup: ucsc.class.cmp241 (use it, too!) Optionally, also follow:
comp.ai
Prereqs:
Grad. Standing, or instructor's permission.
Previous AI course is helpful but not required.
Required:
"Artificial Intelligence: A Modern Approach"
Stuart Russell and Peter Norvig
Prentice Hall, December 1994. ISBN 0-13-103805-2 See Web page for book:
(includes Lisp code) http://http.cs.berkeley.edu/~russell/aima.html
(CMP240 = approximately chapters 1-5, 13, 18, 20 of above.)
Also Chapters 1-5 and 10 from the book Probability Theory:
The Logic of Science available at:
http://omega.albany.edu:8008/JaynesBook.html.
(you may find other chapters interesting as well).
For information on Charles Peirce's work on logic and existential graphs:
http://plato.stanford.edu/entries/peirce-logic/
For state-of-art on automatic theorem proving (and the programs OTTER and GANDALF) please see:
http://stlinux.ouhk.edu.hk/~logic/logicres.html
http://arp.anu.edu.au/~jks/
http://www-formal.stanford.edu/clt/ARS/ars-db.html
Very good references:
Rich, Elaine.
Knight, Kevin
Artificial Intelligence., Second Edition!
McGraw-Hill Book Co.,New York,1990.
Elementary Probability - David Stirzaker Cambridge University Press
- 1994.
References: (You may wish to get one for a different point-of-view and a focus on expert system technology).
Expert Systems: Principles and Programming
Joseph Giarrtano and Gary Riley
PWS-Kent Publishing Company
Boston, 1989.
Introduction to Expert Systems
Peter Jackson 1990 Addison-Wesley
CIS240 reader.
Expert Systems for Experts:
Parsaye and Chignell
Building Expert Systems
Hayes-Roth, Waterman, Lenat
Addison-Wesley, 1983.
Introduction to Artificial Intelligence
Charniak and McDermott
Addison-Wesley, 1986
Principles of Artificial Intelligence
Nils J. Nilsson
Tioga Press, Palo Alto,CA 1980.
Readings in Artificial Intelligence
Webber,B.L. and Nilsson, N .J.
Tioga Press, 1982.
Barr,Avron, Cohen,P.R. and Feigenbaum E. A.
The Handbook of Artificial Intelligence, Volumes I,II and III.
William Kaufmann,Inc., Los Altos, CA 1981 and 1982.
You may enjoy reading the paper:
Concepts of Logical AI by John McCarthy.
http://www-formal.stanford.edu/pub/jmc/concepts-ai.html
(Note: The instructor does not necessarily agree 100% with the Logical school.)
During the course there will be a multi-stage programming project, regular homework problems and a final exam. The projects will be tailored to the individual student's interests within the nature of the course material and the instructor's discretion. The development of an adaptive graph-based theorem prover in Lisp is a possible project.
Course Evaluation:
Written Homework %45
Programming Project %30
Final Exam 25%
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.
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.
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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, chapters (ET) are assigned from the book, Probability Theory: The Logic of Science available at: http://omega.albany.edu:8008/JaynesBook.html.
EC= not required, extra credit.
Due dates for project to be announced. There will be a series of project checkpoints.
I. Introduction to Expert Systems
Text: Chapters 1-3 are mainly review. 6-7.
Exercises 2.1, 3.5, 3.16(EC) 6.1, 6.3(no need to show work). 6.5, 6.7,
7.2, 7.4, 7.5. Due: Fri. Jan. 15
ET: Chapter 10. No exercises.
1. (Mon. January 4)
2. (Weds. January 6)
3. (Fri. January 8)
II. Knowledge Bases and Inference
Text: Chapters 9-10: 9.1, 9.4-9.9 10.2,10.3,10.5
ET: Chapter 1-2: 2.1-2.3
Due: Friday January 29
4. (Mon. January 11)
5. (Weds. January 13)
6. (Fri. January 15)
--. (Mon. January 18) No class. Holiday.
7. (Weds. January 20)
8. (Fri. January 22)
III. Reasoning Under Uncertainty
Chapters 14-16 14.1-14.4 14.7-14.9 14.11,14.12 15.3 16.2, 16.6, 16.10
(list may be shortened)
ET: 3: 3.1-3.5
Due: Friday February 12
9. (Mon. January 25)
10. (Weds. January 27)
11. (Fri. January 29)
12. (Mon. February 1)
13. (Weds. February 3)
14. (Fri. February 5)
IV. Pattern Matching, Analogy and Retrieval
Text: Papers and problems to be handed out in class.
ET: 4: 4.1-4.4
Due: Friday February 26
15. (Mon. February 8)
16. (Weds. February 10).
17. (Fri. February 12).
V. ADVANCED TOPICS
ET: Chapter 5: 5.1-5.3
(possibly more problems and/or alternative topic problems to be announced).
Due: Friday March 12
--. (Mon. February 15) No class. Holiday.
18. (Tues. February 16) Class. Exchange Day.
19. (Wed. February 17)
20. (Fri. February 19)
21. (Mon. February 22)
22. (Wed. February 24)
23. (Fri. February 26)
24. (Mon. March 1)
25. (Wed. March 3)
26. (Fri. March 5)
27. (Mon. March 8)
28. (Wed. March 10)
29. (Fri. March 12) REVIEW
Final Exam - Wednesday, March 17 , 8am-11am at regular classroom site.
The final exam will come from material in the lectures, readings, projects and textbook. But lecture material will be the main focus. The exam may be open book, closed book or take home.