Foundations of Knowledge Acquisition pp 227-262 | Cite as
Problem Solving via Analogical Retrieval and Analogical Search Control
- 1 Citations
- 253 Downloads
Abstract
In this chapter we describe Eureka, a problem solver that uses analogy as its basic reasoning and learning process. Eureka introduces a learning mechanism called analogical search control, and uses a model of memory based on spreading activation to retrieve analogies and solve problems. These relatively simple mechanisms allow the system to account for a number of psychological phenomena in problem solving. In this chapter we focus on some of the computational aspects of the system. To this end, we provide a full description at theoretical and implementation levels, and present the results of some experiments that explore the model’s computational behavior.
Keywords
Problem Solver Semantic Network Analogical Reasoning Spreading Activation Retrieval TimePreview
Unable to display preview. Download preview PDF.
References
- Anderson, J. R. (1974). Retrieval of propositional information from long-term memory. Cognitive Psychology, 5, 451–474.CrossRefGoogle Scholar
- Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
- Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
- Anderson, J. R. & Thompson, R. (1989). Use of analogy in a production system architecture. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning. Cambridge, England: Cambridge University Press.Google Scholar
- Carbonell, J. G. (1983). Learning by analogy: Formulating and generalizing plans from past experience. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.Google Scholar
- Carbonell, J. G. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (vol. 2). Los Altos, CA: Morgan Kaufmann.Google Scholar
- Collins, A., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–248.CrossRefGoogle Scholar
- Ernst, G., & Newell, A. (1969). GPS: A case study in generality and problem solving. New York: Academic Press.Google Scholar
- Falkenhainer, B. C. (1989). Learning from physical analogies: A study in analogy and the explanation process. Doctoral dissertation, University of Illinois at Urbana-Champaign.Google Scholar
- Falkenhainer, B., Forbus, K. D., & Gentner, D. (1986). The structure-mapping engine. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 272–277). Philadelphia: Morgan Kaufmann.Google Scholar
- Fikes, R. E., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–208.zbMATHCrossRefGoogle Scholar
- Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.CrossRefGoogle Scholar
- Hall, R. P. (1989). Computational approaches to analogical reasoning: A comparative analysis. Artificial Intelligence, 39, 39–120.zbMATHCrossRefGoogle Scholar
- Hammond, K. J. (1988). Case-based planning: An integrated theory of planning, learning, and memory (Doctoral dissertation, Yale University, 1986). Dissertation Abstracts International, 48, 3025B.Google Scholar
- Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.Google Scholar
- Holyoak, K. J., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory and Cognition, 15, 332–340.Google Scholar
- Holyoak, K. J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13, 295–355.CrossRefGoogle Scholar
- Jones, R. M. (1989). A model of retrieval in problem solving. Doctoral dissertation, University of California, Irvine.Google Scholar
- Jones, R. M. & Langley, P. (1991). An integrated model of retrieval and problem solving. Manuscript submitted for publication.Google Scholar
- Jones, R. M. & VanLehn, K. (1991). Strategy shifts without impasses: A computational model of the sum-to-min transition. In Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 358–363). Chicago: Lawrence Erlbaum.Google Scholar
- Kolodner, J. L., Simpson, R. L., & Sycara, K. (1985). A process model of case-based reasoning in problem solving. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 284–290). Los Angeles: Morgan Kaufmann.Google Scholar
- Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986a). Chunking in Soar: The anatomy of a general learning mechanism. Machine Learning, 1, 11–46.Google Scholar
- Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986b). Universal sub-goaling and chunking: The automatic generation and learning of goal hierarchies. Hingham, MA: Kluwer Academic.Google Scholar
- Langley, P. (1985). Learning to search: From weak methods to domain-specific heuristics. Cognitive Science, 9, 217–260.CrossRefGoogle Scholar
- Langley, P., & Allen, J. A. (1991). The acquisition of human planning expertise. In L. A. Birnbaum & G. C. Collins (Eds.), Machine Learning: Proceedings of the Eighth International Workshop (pp. 80–84). Evanston, IL: Morgan Kaufmann.Google Scholar
- Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90, 227–234.CrossRefGoogle Scholar
- Minton, S. (1989). Learning effective search control knowledge: An explanation-based approach (Doctoral dissertation, Carnegie Mellon University, 1988). Dissertation Abstracts International, 49, 4906B–4907B.Google Scholar
- Mitchell, T. M, Utgoff, P. E., & Banerji, R. (1983). Learning by experimentation: Acquiring and refining problem-solving heuristics. In R. S. Michalski, J. G. Carbonell, T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.Google Scholar
- Neches, R. (1982). Models of heuristic procedure modification (Doctoral dissertation, Carnegie Mellon University, 1981). Dissertation Abstracts International, 43, 1645B.Google Scholar
- Ohlsson, S. (1987). Transfer of training in procedural learning: A matter of conjectures and refutations? In L. Bole (Ed.), Computational models of learning. Berlin: Springer-Verlag.Google Scholar
- Schank, R. C. (1982). Dynamic memory. Cambridge, England: Cambridge University Press.Google Scholar
- VanLehn, K., & Jones, R. M. (in press). Integration of explanation-based learning of correctness and analogical search control. In S. Minton & P. Langley (Eds.), Proceedings of the symposium on learning, planning and scheduling. Los Altos, CA: Morgan Kaufmann.Google Scholar