Syllabus for Reasoning and Learning in Cognitive Systems

Hard-copy readings will be available in a box outside Gates 222 for those who do not pick them up in class.

Meeting 1 [1/10]. List structures, pattern matching, and symbolic processing.

Newell, A., & Simon, H. A. (1976). Computer science as empirical enquiry: Symbols and search. Communications of the ACM, 19, 113-126.

Meeting 2 [1/12]. Monotonic inference

Slagle, J. R. (1963). A heuristic program that solves symbolic integration problems in freshman calculus. In E. A. Feigenbaum & J. Feldman (Eds.), Computers and Thought. New York: McGraw-Hill, 1963.
Genesereth, M. R., & Ginsberg, M. L. (1985). Logic programming. Communications of the ACM, 28, 933-941.
[optional] Polk, T. A., & Newell, A. (1988). Modeling human syllogistic reasoning in Soar. Proceedings of the Tenth Annual Conference of the Cognitive Science Society (pp. 181-187). Montreal, Quebec: Lawrence Erlbaum.

Meeting 3 [1/17]. Recognize-act systems

Simon, H. A. (1975). The functional equivalence of problem solving skills. Cognitive Psychology, 7, 268-288.
Young, R., & O'Shea, T. (1981). Errors in children's subtraction. Cognitive Science, 5, 153-177.

Meeting 4 [1/19]. Problem solving and planning

Newell, A., & Simon, H. A. (1961). GPS, A program that simulates human thought. In H. Billing (Ed.), Lernende automaten. Munich: Oldenbourg KG. Reprinted in E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought. New York: McGraw-Hill, 1963.
Nau, D., Cao, Y., Lotem, A., & Munoz-Avila, H. (1999). SHOP: A simple hierarchical ordered planner. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 968-973).
[optional] VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. I. Posner (Ed.), Foundations of cognitive science. Cambridge, MA: MIT Press.

Meeting 5 [1/24]. Analytical learning for monotonic inference

Samuelsson, C., & Rayner, M. (1991). Quantitative evaluation of explanation-based learning as an optimisation tool for a large-scale natural language system. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 609-615). Sydney: Morgan Kaufmann.
Wogulis, J., & Langley, P. (1989). Improving efficiency by learning intermediate concepts. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 657-662). Detroit, MI: Morgan Kaufmann.
[optional] Mooney, R. J. (1989). The effect of rule use on the utility of explanation-based learning. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 725-730). Detroit: Morgan Kaufmann.

Meeting 6 [1/26]. Relational learning/Inductive logic programming

Holder, L. B., Cook, D. J., & Bunke, H. (1992). Fuzzy substructure discovery. Proceedings of the Ninth International Conference on Machine Learning (pp. 218-223). Aberdeen, Scotland: Morgan Kaufmann.
Quinlan, J. R. (1990). Determinate literals in inductive logic programming. Proceedings of the Eighth International Workshop on Machine Learning (pp. 442-446). Evanston, IL: Morgan Kaufmann.
Dzeroski, S., & Lavrac, N. (1991). Learning relations from noisy examples: An empirical comparison of LINUS and FOIL. Proceedings of the Eighth International Workshop on Machine Learning (pp. 399-402). Evanston, IL: Morgan Kaufmann.
First exercise due.

Meeting 7 [1/31]. Theory refinement and revision

Richards, B. L., & Mooney, R. J. (1991). First-order theory revision. Proceedings of the Eighth International Workshop on Machine Learning (pp. 447-451). Evanston, IL: Morgan Kaufmann.
Wogulis, J. (1991). Revising relational domain theories. Proceedings of the Eighth International Workshop on Machine Learning (pp. 462-466). Evanston, IL: Morgan Kaufmann.
[optional] Sleeman, D. H., & Craw, S. (1990) Automating the refinement of knowledge-based systems. Proceedings of the Ninth European Conference on Artificial Intelligence (pp. 167-172).

Meeting 8 [2/2]. Combining analytical and empirical methods

Pazzani, M., & Brunk, C. (1993). Finding accurate frontiers: A knowledge-intensive approach to relational learning. Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. 328-334). Washington, D.C: AAAI Press.
Zelle, J. M., & Mooney, R. J. (1993). Combining FOIL and EBG to speed up logic programs. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (pp. 1106-1111). Chambery, France: Morgan Kaufmann.

Meeting 9 [2/7]. Analogical reasoning

Gentner, D., & Forbus, K.. (1991). MAC/FAC: A model of similarity-based retrieval. Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 504-509). Chicago: Lawrence Erlbaum.
Hickman, A. K., & Larkin, J. H. (1990). Internal analogy: A model of transfer within problems. Proceedings of the Twelfth Annual Conference of The Cognitive Science Society(pp. 53-60). Hillsdale, NJ: Lawrence Erlbaum.
[optional] Keane, M., & Brayshaw, M. (1988). The incremental analogy machine. Proceedings of the Third European Working Session on Learning (pp. 53-62). London: Pitman.

Meeting 10 [2/9]. Learning for qualitative reasoning

Ramachandran, S., Mooney, R. J., & Kuipers, B. J. (1994). Learning qualitative models for systems with multiple operating regions. Proceedings of the Eight International Workshop of Qualitative Reasoning about Physical Systems (pp. 212-223). Nara, Japan.
Goel, A. K. (1991). Model revision: A theory of incremental model learning. Proceedings of the Eighth International Workshop on Machine Learning (pp. 605-609). Evanston, IL: Morgan Kaufmann.

Meeting 11 [2/14]. Learning state-space heuristics

Langley, P. (1982). Strategy acquisition governed by experimentation. Proceedings of the European Conference on Artificial Intelligence (pp. 171-176). Orsay, France.
Laird, J. E., Rosenbloom, P. S., Newell, A. (1984). Towards chunking as a general learning mechanism. Proceedings of the Fourth National Conference on Artificial Intelligence (pp. 188-192). Austin, TX: AAAI Press.
Second exercise due.

Meeting 12 [2/16]. Learning state-space macro-operators

Iba, G. A. (1985). Learning by discovering macros in puzzle solving. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 640-642). Los Angeles: Morgan Kaufmann.
Lee, F. J., & Taatgen, N. A. (2002). Multi-tasking as skill acquisition. Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 572-577). Mahwah, NJ: Erlbaum.
[optional] Minton, S. (1985). Selectively generalizing plans for problem solving. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 596-599). Los Angeles: Morgan Kaufmann.

Meeting 13 [2/21]. Learning in game playing

Epstein, S. L. (1990). Learning plans for competitive domains. Proceedings of the Seventh International Conference on Machine Learning (pp. 190-197). Austin, TX: Morgan Kaufmann.
Fawcett, T., & Utgoff, P. E. (1992). Automatic feature generation for problem solving systems. Proceedings of the Ninth International Workshop on Machine Learning (pp. 144-153). Aberdeen, Scotland: Morgan Kaufmann.
[optional] Gobet, F. Studies in chess expertise. Centre for Research in Development, Instruction, and Training, Nottingham University, Nottingham, UK.
[optional] George, M., & Schaeffer, J. (1990). Chunking for experience. International Computer Chess Association Journal, 13, 123-132.

Meeting 14 [2/23]. Learning for means-ends analysis

Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. Proceedings of the Seventh National Conference on Artificial Intelligence (pp. 564-569). Saint Paul, MN: AAAI Press.
Jones, R. M., & VanLehn, K. (1991). A computational model of acquisition for children's addtion strategies. Proceedings of the Eighth International Workshop on Machine Learning (pp. 65-69). Evanston, IL: Morgan Kaufmann.

Meeting 15 [2/28]. Learning for partial-order planning

Katukam, S. & Kambhampati, S. (1994). Learning explanation-based search control rules for partial order planning. Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 582-587). Seattle, WA: AAAI Press.
Estlin, T. A., & Mooney, R. J. (1997). Learning to improve both efficiency and quality of planning. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 1227-1232). Nagoya, Japan.
[optional] Huang, Y., Selman, B. & Kautz, H. (2000). Learning declarative control rules for constraint-based planning. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 415-422). Stanford, CA: Morgan Kaufmann.

Meeting 16 [3/2]. Analogical problem solving

Veloso, M. M. (1994). Flexible strategy learning: Analogical replay of problem solving episodes. Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 595-600). Seattle, WA: AAAI Press.
Jones, R., & Langley, P. (1995). Retrieval and learning in analogical problem solving. Proceedings of the Seventeenth Conference of the Cognitive Science Society (pp. 466-471). Pittsburgh: Lawrence Erlbaum.

Meeting 17 [3/7]. Learning for hierarchical task networks

Reddy, C., & Tadepalli, P. (1997). Learning goal-decomposition rules using exercises. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 278-286). Nashville, TN: Morgan Kaufman.
Choi, D., & Langley, P. (2005). Learning teleoreactive logic programs from problem solving. Proceedings of the Fifteenth International Conference on Inductive Logic Programming (pp. 51-68). Bonn, Germany: Springer.
Third exercise due.

Meeting 18 [3/9]. Learning and abstraction in problem solving

Knoblock, C. A. (1990). Abstracting the Tower of Hanoi. Proceedings of the Workshop on Automatic Generation of Approximations and Abstractions. Boston, MA.
Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. Cognitive Science, 14, 511-550.
[optional] Unruh, A., Washington, R., & Rosenbloom, P. (1996). A framework for automatic abstraction. In M. Ghallab & A. Milani (Eds.), New directions in AI planning. Amsterdam: IOS Press.

Meeting 19 [3/14]. Representation and insight in problem solving

Langley, P., & Jones, R. (1988). A computational model of scientific insight. In R. Sternberg (Ed.), The nature of creativity. Cambridge University Press.
[optional] Cronin, M. A. (2004). A model of knowledge activation and insight in problem solving. Complexity, 9, 17-24

Meeting 20 [3/16]. Concluding discussion

Fourth exercise due Sunday, 3/19.

For more information, send electronic mail to langley@csli.stanford.edu