Abstract
This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture.
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References
Minton, S. (1990). Quantitative results concerning the utility of explanation-based learning. Artificial Intelligence, 42, 363–391.
Laird, J. E., Rosenbloom, P. S., and Newell, A., (1986). Chunking in SOAR: The anatomy of a general learning mechanism. Machine Learning, 1, 11–46.
de Groot, A. N. (1946). Het denken van den schaker[Thought and choice in chess]. Amsterdam: North-Holland.
Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. Cognitive Science, 14, 511–550.
Cheng, P. C-H. (1996). Scientific discovery with law-encoding diagrams. Creativity Research Journal, 9, 145–162.
Cheng, P. C-H. (1998). A framework for scientific reasoning with law encoding diagrams: Analysing protocols to assess its utility. In M. A. Gernsbacher & S. J. Derry (Eds.) Proceedings of the Twentieth Annual Conference of the Cognitive Science Society(pp. 232–235 ). Mahwah, NJ: Erlbaum.
Cheng, P. C-H. (submitted). Electrifying representations for learning: An evaluation of AVOW diagrams for electricity.
Larkin, J. H. & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.
Tabachneck-Schijf, H. J. M., Leonardo, A. M., & Simon, H. A. (1997). CaMeRa: A computational model of multiple representations. Cognitive Science, 21, 305–350.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.
Simon, H. A. & Gilmartin, K. J. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5, 29–46.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.
Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision. McGraw-Hill, New York.
Koedinger, K. R., & Anderson, J. R. (1993). Reifying implicit planning in geometry: Guidelines for model-based intelligent tutoring system design. In S. P. Lajoie & S. J. Derry (Eds.), Computers as Cognitive Tools, Lawrence Erlbaum, New Jersey.
Gobet, F. (1998). Memory for the meaningless: How chunks help. In M. A. Gernsbacher & S. J. Derry (Eds.) Proceedings of the Twentieth Annual Conference of the Cognitive Science Society(pp. 398–403 ). Mahwah, NJ: Erlbaum.
Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305–336.
Lebowitz, M. (1987). Experiments with incremental concept formation: UNIMEM. Machine Learning, 2, 103–138.
Gobet, F. (1996). Discrimination nets, production systems and semantic networks: Elements of a unified framework. Proceedings of the Second International Conference of the Learning Sciences(pp. 398–403). Evanston, III: Northwestern University.
Kieras, D. A. (1993). Learning schemas from explanations in practical electronics. In S. Chipman & A. L. Meyrowitz (Eds.) Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning, Kluwer Academic Publishers.
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Lane, P.C.R., Cheng, P.CH., Gobet, F. (2000). Learning Perceptual Schemas to Avoid the Utility Problem. In: Bramer, M., Macintosh, A., Coenen, F. (eds) Research and Development in Intelligent Systems XVI. Springer, London. https://doi.org/10.1007/978-1-4471-0745-3_5
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DOI: https://doi.org/10.1007/978-1-4471-0745-3_5
Publisher Name: Springer, London
Print ISBN: 978-1-85233-231-0
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