Bruce McLaren's ISP Comps Reading List
Reading List for the Ph.D. Comprehensive Examination
Bruce McLaren
Intelligent Systems Program (ISP)
University of Pittsburgh
January, 1995
This paper contains my proposed reading list for the ISP
comprehensive examination. The reading list is divided into two
major sections (i.e., Case-Based Reasoning and Knowledge
Representation) and two minor sections (i.e. Machine Learning and
Knowledge Acquisition). Although my dissertation topic is not yet
defined, I believe these four areas will have the greatest impact on the
content of the thesis. The two major areas, Case-Based Reasoning and
Knowledge Representation, are the subdisciplines within AI that will
have the greatest influence on my dissertation. Machine Learning and
Knowledge Acquisition are likely to play a lesser, but still important,
role in my research. (Note: I have concentrated on symbolic forms of
Machine Learning, excluding such areas as genetic algorithms and
connectionist learning, since my dissertation will be focused on
symbolic knowledge and expertise.)
The preparation of the content of this document was aided by reference
to the Intelligent Systems Program (ISP) comprehensive exam reading
lists of Vincent Aleven, R. Michael Young, Daniel Hennessy, and
Violetta Cavalli-Sforza. Also, the reading lists for the ISP core courses
Knowledge Representation and Problem Solving, Planning, and
Search provided additional references.
1. Case-Based Reasoning
1.1. Overview
Ashley, K. D. Case-Based Reasoning and its Implications for Legal
Expert Systems, Artificial Intelligence and Law, 1, no. 2-3 (1993).
Kolodner, J. Case-Based Reasoning. Morgan Kaufmann, Inc., 1993.
1.2. Statistically-Oriented CBR
Stanfill, C. Memory-Based Reasoning Applied to English
Pronunciation. In Proceedings of AAAI-87, pages 577-581. American
Association of Artificial Intelligence. Seattle. 1987.
1.3. Exemplar-based CBR
Bariess, E. R. Exemplar-Based Knowledge Acquisition - A Unified
Approach to Concept Representation, Classification, and Learning. San
Diego, CA: Academic Press, 1989. (This book also appears in the
Knowledge Acquisition section of the Reading List).
1.4. Model-based CBR
Koton, P. Using Experience in Learning and Problem Solving. Ph.D.
Thesis, Massachusetts Institute of Technology, 1988.
1.5. Planning / Design-oriented CBR
Carbonell, J. G. Derivational Analogy: A Theory of Reconstructive
Problem Solving and Expertise Acquisition. In Machine Learning: An
Artificial Intelligence Approach, Volume II, edited by R. S. Michalsky, J.
G. Carbonell, and T. M. Mitchell, 371-392. San Mateo, CA: Morgan
Kaufmann, 1986.
Hammond, K. J. Case-Based Planning: Viewing Planning as a Memory
Task. San Diego, CA: Academic Press, 1989.
Veloso, M. M. Learning by Analogical Reasoning in General Problem
Solving. Ph.D. Thesis, Carnegie Mellon University, 1992.
1.6. Adversarial / Precedent-Based CBR
Ashley, K. D. Modeling Legal Argument: Reasoning with Cases and
Hypotheticals. MIT Press, Cambridge, 1990.
Branting, L. K. Building Explanations from Rules and Structured
Cases. International Journal of Man-Machine Studies, 34 (6): 797-837,
1991.
Rissland, E. L., Skalak, D. B., and Friedman, M. T. BankXX: A Program
to Generate Argument through Case-Based Search. In Proceedings of
the Fourth International Conference on Artificial Intelligence and Law
(ICAIL), Amsterdam, The Netherlands, 1993.
Rissland, E. L., Skalak, D. B., and Friedman, M. T. Heuristic Harvesting
of Information for Case-Based Argument. In AAAI-94, Seattle. 1994.
1.7. Case-Based Explanation
Burke, R. and Kass, A. Tailoring Retrieval to Support Case-Based
Teaching. In AAAI-94, Seattle, Washington. 1994.
Kass, A. M., Leake, D. B., and Owens, C. SWALE: A Program that
Explains. In R. C. Schank, Explanation Patterns: Understanding
Mechanically and Creatively, Appendix, 232-254. Hillsdale, NJ:
Lawrence Erlbaum, 1986.
Leake, D. B. An Indexing Vocabulary for Case-Based Explanation. In
AAAI-91: Proceedings, Ninth National Conference on Artificial
Intelligence, 10-15. Menlo Park, CA: AAAI Press, 1991.
1.8. Integration of Case-Based and Rule-Based Reasoning
Branting, L. K. and Porter, B. W. Rules and Precedents as
Complementary Warrants. In Proceedings from AAAI-91. 1991.
Golding, A. R. and Rosenbloom, P. S. Improving Rule-Based Systems
through Case-Based Reasoning. In Proceedings from AAAI-91. 1991.
Rissland, E. L. and Skalak, D. B., CABARET: Statutory Interpretation in
a Hybrid Architecture. In International Journal of Man-Machine
Studies, 34 (6): 839-887, 1991.
2. Knowledge Representation
2.1. Overview and General Readings
Brachman, R. J., The Future of Knowledge Representation, In
Proceedings of AAAI-90, 1990.
Ginsberg, M., Essentials of Artificial Intelligence, Morgan Kaufmann.
Chapters 6-9 1993.
2.2. Logic as Representation and Inference
Genesereth, M. and Nilsson, N. Logical Foundations of Artificial
Intelligence, Morgan Kaufmann, Chapters 1-4, 1987.
Fikes, R. and Nilsson, N., STRIPS: A New Approach to the Application
of Theorem Proving to Problem Solving. IJCAI-71, London, England,
1971.
2.3. Frames, Structured Inheritance Networks, and Classification
Woods, W. and Schmolze, J. "The KL-ONE family." In Fritz Lehmann,
ed., Semantic Networks in Artificial Intelligence, Pergamon Press, 133-
177. 1992.
Brachman, R. J., McGuinness, D. L., Patel-Schneider, P. F., Resnick, L.
A., and Borgida, A., Living with CLASSIC: When and How to Use a
KL-ONE-Like Language. In Principles of Semantic Networks, J. Sowa,
Morgan Kaufmann, 1990.
Clancey, W. J., Heuristic Classification. InArtificial Intelligence 27,
285-350. 1985.
MacGregor, R. M., The Evolving Technology of Classification-Based
Knowledge Representation Systems. Principles of Semantic Networks:
Explorations in the Representation of Knowledge, Chapter 13, John
Sowa, editor, Morgan Kaufmann, 1990.
Sowa, J. F., Towards the Expressive Power of Natural Language.
Principles of Semantic Networks: Explorations in the Representation of
Knowledge, John Sowa, editor, Morgan Kaufmann, 1990.
2.4. Nonmonotonic Reasoning
Ginsberg, M., In Nonmonotonic Reasoning,, Chap. 1 RIntroductionS
Morgan Kaufmann, 1988.
Brachman, R. J., RI Lied About the TreesS Or, Defaults and Definitions
in Knowledge Representation. The AI Magazine, Fall, 1985.
Reiter, R., A Logic for Default Reasoning. In Artificial Intelligence,
13:81-132, 1980.
2.5. Inheritance
Thomason, R., NETL and Subsequent Path-Based Inheritance Theories.
Computers Math. Applic. Vol. 23, No 2-5, pages 179-204, 1992.
2.6. Hybrid Approaches
Yen, J., Juang, H., and MacGregor, R. Using Polymorphism to Improve
Expert System Maintainability. In IEEE Expert, April, 1991.
Yen, J., Neches, R. and MacGregor, R. CLASP: Integrating Term
Subsumption Systems and Production Systems. In IEEE Transactions
on Knowledge and Data Engineering, Vol. 3, No. 1, March, 1991.
2.7. Conceptual Dependency
Kolodner, J., Maintaining Organization in a Dynamic Long-Term
Memory. Cognitive Science 7, 243-280. 1983.
Lytinen, S.L., Conceptual dependency and its descendents. In Fritz
Lehmann, ed., Semantic Networks in Artificial Intelligence, Pergamon
Press, 403-418. 1992.
3. Machine Learning
3.1. Overview and General Readings
Carbonell, J. G., Introduction: Paradigms for Machine Learning. In
Machine Learning: Paradigms and Methods. 1-9. Elsevier Science
Publishers B. V. 1990.
Charniak, E. and McDermott, D. Introduction to Artificial Intelligence.
Chap 11. Addison-Wesley Publishing Company. 1987.
Laird, J. E., Rosenbloom, P. S., and Newell, A. Chunking in SOAR: The
Anatomy of a General Learning Mechanism. Machine Learning 1: 11-
46. 1986.
Michalski, R. S., Toward a Unified Theory of Learning: Multistrategy
Task-Adaptive Learning. In ONR Workshop on Knowledge
Acquisition, Arlington, VA, November 6-7, 1989.
3.2. Explanation-Based Learning
Minton, S., Carbonell, J. G., Knoblock, C. A., Kuokka, D. R., Etzioni, O.,
and Gil, Y.,
Explanation-Based Learning: A Problem Solving Perspective. In Machine
Learning:
Paradigms and Methods. 63-118. Elsevier Science Publishers B. V. 1990.
Minton, S., Quantitative Results Concerning the Utility of EBL. In the
Proceedings of AAAI-88: The Sixth National Conference on Artificial
Intelligence, Morgan Kaufmann, 1988.
Mitchell, T. M., Keller, R. M. and Kedar-Cabelli, S. T., Explanation-
Based Generalization: A Unifying View. Machine Learning 1: 47-80.
1986.
3.3. Induction
Mitchell, T. M., Utgoff, P. E., and Banerji, R. Learning by
Experimentation: Acquiring and Refining Problem-Solving Heuristics.
In Machine Learning: An Artificial Intelligence Approach, edited by R.
Michalski, J. G. Carbonell, and T. M. Mitchell, Tioga. 1983.
Zelle J. and Mooney R. Combining FOIL and EBG to Speed-up Logic
Programs, In Proceedings of IJCAI-93.
Quinlan, J. R., Induction of Decision Trees. Machine Learning 1: 81-106.
1986.
Skalak, D. B. and Rissland, E. L. Inductive Learning in a Mixed
Paradigm Setting. In AAAI-90. 840-847. 1990.
3.4. Case-Based Learning
Hammond, K., Converse, T., Marks, M., and Seifert, C. M.
Opportunism and Learning. Machine Learning, 10: 279-309. 1993.
Porter, B. W., Bareiss, R., and Holte, R. C.. Concept Learning and
Heuristic Classification in Weak-Theory Domains. Artificial
Intelligence 45, 229-263. 1990.
Schank, R. C. and Leake, D. B. Creativity and Learning in a Case-Based
Explainer. In Machine Learning: Paradigms and Methods. 353-385.
Elsevier Science Publishers B. V. 1990.
Veloso, M., Flexible Strategy Learning: Analogical Replay of Problem
Solving Episodes. In AAAI-94, Seattle, Washington. 1994.
4. Knowledge Acquisition
4.1. Overview
Boose, J. H., A Survey of Knowledge Acquisition Techniques and
Tools. AAAI Knowledge Acquisition for Knowledge-Based Systems
Workshop, Banff, 1988.
4.2. Expertise
Anderson, J. R., Development of Expertise. In Cognitive Psychology
and Its Implications, Chapter 9, 256-288, W. H. Freeman and Co., 1990.
Wielinga, B. J., Schreiber, A. T., and Breuker, J. A., KADS: A Modelling
Approach to Knowledge Engineering. In Knowledge Acquisition and
Learning: Automating the Construction and Improvement of Expert
Systems. Edited by B. Buchanan and D. Wilkins. 1993.
4.3. Expert Systems
Clancey, W. J. Acquiring, Representing, and Evaluating a Competence
Model of Diagnostic Strategy. In Knowledge Acquisition and Learning:
Automating the Construction and Improvement of Expert Systems.
Edited by B. Buchanan and D. Wilkins. 1993.
McDermott, J. Preliminary Steps Toward a Taxonomy of Problem-
Solving Methods. In Automating Knowledge Acquisition for Expert
Systems, 225-256, editor Marcus, S., Kluwer Academic Publishers. 1988.
Swartout, W. R. XPLAIN: A System for Creating and Explaining Expert
Consulting Programs. Artificial Intelligence 21, 285-325. 1983.
4.4. Interactive Elicitation
Davis, R. Interactive Transfer of Expertise: Acquisition of New
Inference Rules. Artificial Intelligence 12, 121-157. 1979.
Eshelman, L., Ehret, D., McDermott, J., and Tan, M. MOLE: A
Tenacious Knowledge-Acquisition Tool. In Knowledge Acquisition
and Learning: Automating the Construction and Improvement of
Expert Systems. Edited by B. Buchanan and D. Wilkins. 1993.
Bariess, E. R. Exemplar-Based Knowledge Acquisition - A Unified
Approach to Concept Representation, Classification, and Learning. San
Diego, CA: Academic Press, 1989. (This book also appears in the Case-
Based Reasoning section of the Reading List).
4.5. Apprenticeship
Mitchell, T. M., Mahadevan, S., and Steinberg, L. I., LEAP: A Learning
Apprentice For VLSI Design. In Knowledge Acquisition and Learning:
Automating the Construction and Improvement of Expert Systems.
Edited by B. Buchanan and D. Wilkins. 1993.
Wilkins, D. C., Knowledge Base Refinement as Improving an
Incomplete and Incorrect Domain Theory. In Knowledge Acquisition
and Learning: Automating the Construction and Improvement of
Expert Systems. Edited by B. Buchanan and D. Wilkins. 1993.
4.6. Case-Based Knowledge Acquisition
Sycara, K. and Miyashita, K., Case-Based Acquisition of User
Preferences for Solution Improvement in Ill-Structured Domains. In
AAAI-94, Seattle, Washington. 1994.