Advertisement

Analogy — From a Unified Perspective

  • Smadar Kedar-Cabelli
Chapter
Part of the Synthese Library book series (SYLI, volume 197)

Abstract

A remarkable ability of people is to easily understand new situations by analogy to old ones, to comprehend metaphors, and to solve problems based on previously solved, analogous problems. All of these may be considered abilities of analogical reasoning (AR). In artificial intelligence (AI), we would like to understand these so that we may capture them in intelligent machines.

Keywords

Causal Structure Analogical Reasoning Causal Network Analogous Base Analogical Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acosta, R. D., Huhns, M. H., and Liuh, S. L. (1986), ‘Analogical reasoning for digital system synthesis’, in Proceedings of the 1986 International Conference on ComputerAided Design (ICCAD-86). Google Scholar
  2. Becker, J. D. (1969), ‘The modelling of simple analogic and inductive processes in a semantic memory system’, in Proceedings IJCAI-., IJCAI, Washington, DC, pp. 655–668.Google Scholar
  3. Burstein, M. H. (1983), ‘A model of learning in analogical problem solving’, in Proceedings AAAI-8., AAAI, Washington, DC, pp. 45–48.Google Scholar
  4. Burstein, M. H. (1985), Learning by Reasoning from Multiple Analogie., PhD thesis, Yale University.Google Scholar
  5. Burstein, M. H. (1986), ‘Concept formation by incremental analogical reasoning and debugging’, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approac. 2, 351–369. Morgan Kaufmann, Los Altos, CA.Google Scholar
  6. Burstein, M. H. (1987), ‘Incremental learning from multiple analogies’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  7. Carbonell, J. G. (1982), ‘Experiential learning in analogical problem solving’, in Proceedings AAAI-8., Pittsburgh, PA, pp. 168–171.Google Scholar
  8. Carbonell, J. G. (1983), ‘Learning by analogy: Formulating and generalizing plans from past experience’, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approac., Tioga, Palo Alto, CA, pp. 137–161.Google Scholar
  9. Carbonell, J. G. (1983), ‘Derivational analogy and its role in problem solving’, in Proceedings AAAI-8., AAAI, Washington, DC, pp. 64–69.Google Scholar
  10. Carbonell, J. G. (1986), ‘Derivational analogy: a theory of reconstructive problem solving and expertise acquisition’, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approac. ., 371–392. Morgan Kaufmann, Los Altos, CA.Google Scholar
  11. Darden, L. (1983), ‘Reasoning by analogy in scientific theory construction’, in Proceedings of the Second International Machine Learning Worksho., AAAI, Allerton House, University of Illinois, Urbana, IL, pp. 32–40.Google Scholar
  12. Darden, L. and Rada, R. (1987), ‘Hypothesis formation via interrelations’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  13. Davies, T. R. and Russell, S. J. (1987), ‘A logical approach to reasoning by analogy’, in Proceedings IJCAI-1., Milan, Italy, pp. 264–270.Google Scholar
  14. DeJong, G. and Mooney, R. (1986), ‘Explanation based learning: an alternative view’, Machine Learnin. .(2), 145–176.Google Scholar
  15. Dyer, G. M. (1983), In-Depth Understandin., MIT Press, Cambridge, MA.Google Scholar
  16. Dyer, G. M. (1983), ‘Understanding stories through morals and remindings’, in Proceedings IJCAI-., Karlsruhe, West Germany, pp. 75–77.Google Scholar
  17. Evans, T. G. (1968), ‘A program for the solution of geometric analogy intelligence test questions’, in Minsky, M. (ed.), Semantic Information Processin., MIT Press, Cambridge.Google Scholar
  18. Falkenhainer, B. (1987), ‘An examination of the third stage in the analogy process: verification-based analogical learning’, in Proceedings IJCAI-1., Milan, Italy, pp. 260–263.Google Scholar
  19. Falkenhainer, B., Forbus, K. D., and Gentner, D. (1986), ‘The structure-mapping engine’, in Proceedings AAAI-8., Philadelphia, PA, pp. 272–277.Google Scholar
  20. Gentner, D. (1983), ‘Structure mapping: a theoretical framework for analogy’, Cognitive Scienc. .(2), 155–170.CrossRefGoogle Scholar
  21. Gentner, D. (1987), ‘Analogical inference and access’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  22. Gentner, D. (1988), ‘The mechanisms of analogical learning’, in Vosniadou, S. and Ortony, A. (eds.), Similarity and Analogical Reasonin., Cambridge University Press, Oxford, Forthcoming.Google Scholar
  23. Gick, M. L. and Holyoak, K. L. (1980), ‘Analogical problem solving’, Cognttive Psycholog. 1., 306–355.CrossRefGoogle Scholar
  24. Gick, M. L. and Holyoak, K. L. (1983), ‘Schema induction and analogical transfer’, Cognitive Psycholog. 15, 1–38.CrossRefGoogle Scholar
  25. Greiner, R. (1985), Learning by Understanding Analogie., PhD thesis, Stanford University.Google Scholar
  26. Greiner, R. (1987), ‘Learning by understanding analogies’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  27. Hall, R. P. (1986), Understanding Analogical Reasoning: Computational Approaches. Technical Report 86–11, Department of Computer and Information Science, University of California — Irvine, Irvine, CA.Google Scholar
  28. Hammond, K. (1986), ‘CHEF: A model of case-based planning’, in Proceedings AAAI-8., Philadelphia, PA, pp. 267–277.Google Scholar
  29. Hobbs, J. R. (1981), ‘Metaphor interpretation as selective inferencing’, in Proceedings IJCAI-., Vancouver, BC, Canada, pp. 85–91.Google Scholar
  30. Holland, J., Holyoak, K., Nisbett, R., and Thagard, P. (1986), Induction: Processes of Inference, Learning, and Discover., MIT Press, Cambridge, MA.Google Scholar
  31. Indurkhya, B. (1985), A Computational Theory of Metaphor Comprehension and Analogical Reasonin., PhD thesis, Boston University. (Also B. U. Technical Report # 85/001).Google Scholar
  32. Indurkhya, B. (1987), ‘Constrained semantic transference’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  33. Kedar-Cabelli, S. (1984), Analogy with Purpose in Legal Reasoning from Precedent., Technical Report LRP-TR-17, Laboratory for Computer Science Research, Rutgers University.Google Scholar
  34. Kedar-Cabelli, S. T. (1985), ‘Purpose-directed analogy’, in Proceedings of the Cognitive Science Society Conferenc., Irvine, CA, pp. 150–159.Google Scholar
  35. Kedar-Cabelli, S. T. (1987), ‘Toward a computational model of purpose-directed analogy’, in Prieditis, A. (ed.), Analogica: Proceedings of the First Workshop on Analogical Reasonin., Morgan Kaufmann, Los Altos, CA.Google Scholar
  36. Kling, R. E. (1971), ‘A paradigm for reasoning by analogy’, Artificial Intelligenc. .(2), 147–178.CrossRefGoogle Scholar
  37. Kolodner, J. L. (1983), ‘Maintaining organization in a dynamic long-term memory’, Cognitive Scienc. ., 243–280.CrossRefGoogle Scholar
  38. Kolodner, J. L. (1983), ‘Reconstructive memory: A computer model’, Cognitive Scienc. ., 281–328.CrossRefGoogle Scholar
  39. Kolodner, J. L. and Simpson, R. L., Jr. (1985), ‘Machine learning research at Georgia Tech: using experience as a guide for problem solving’, in Proceedings of the Third International Machine Learning Worksho., Skytop, PA, pp. 96–99.Google Scholar
  40. Kolodner, J. L., Simpson, R. L., Jr., Sycara-Cyranski, K. (1985), ‘A process model of case-based reasoning in problem-solving’, in Proceedings IJCAI-., Los Angeles, CA, pp. 284–290.Google Scholar
  41. Lebowitz, M. (1986), ‘Concept learning in a rich input domain: generalization-based memory’, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approac. ., 193–214, Morgan Kaufmann, Los Altos, CA.Google Scholar
  42. Minsky, M. (1963), ‘Introduction’, in Feigenbaum, E. A. and Feldman, J. (eds.), Computers and Though., McGraw-Hill, New York.Google Scholar
  43. Mitchell, T. M. and Keller, R. M. and Kedar-Cabelli, S. T. (1986), ‘Explanation-based generalization: A unifying view’, Machine Learnin. .(1), 47–80.Google Scholar
  44. Mostow, D. J. (1988), ‘Automated replay of design plans: Some issues in derivational analogy’, Artificial Intelligence. Forthcoming.Google Scholar
  45. Polya, G. (1945), How to Solve I., Princeton University Press, Princeton, NJ.Google Scholar
  46. Russell, S. J. (1986), Analogical and Inductive Reasonin., PhD thesis, Stanford University.Google Scholar
  47. Schank, R. C. (1982), Dynamic Memory: A Theory of Reminding and Learning in Computers and Peopl., Cambridge University Press, Cambridge, MA.Google Scholar
  48. Simpson, R. L., Jr. (1985), A Computer Model of Case-Based Reasoning in Problem Solving: An Investigation in the Domain of Dispute Mediatio., PhD thesis, Georgia Institute of Technology.Google Scholar
  49. Skorstad, J., Falkenhainer, B., and Gentner, D. (1987), ‘Analogical processing: simulation and empirical corroboration’, in Proceedings AAAI-8., Seattle, WA, pp. 322–326.Google Scholar
  50. Sternberg, R. J. (1977), ‘Component processes in analogical reasoning’, Psychological Revie. 8., 353–378.CrossRefGoogle Scholar
  51. Warrington, J. (1976), Metaphysics — Aristotl., Dent, London.Google Scholar
  52. Winston, P. H. (1979), Learning by Understanding Analogie., Technical Report AI Lab Memo 520, MIT AI Lab, Cambridge, MA.Google Scholar
  53. Winston, P. H. (1980), ‘Learning and reasoning by analogy’, Communications of the AC. 2.(12), 689–702.CrossRefGoogle Scholar
  54. Winston, P. H. (1981), Learning New Principles from Precedents and Exercises: The Detail., Technical Report AI Lab Memo 632, MIT AI Lab, Cambridge, MA.Google Scholar
  55. Winston, P. H. (1982), ‘Learning new principles from precedents and exercises’, Artificial Intelligenc. 1.(3), 321–350.CrossRefGoogle Scholar
  56. Winston, P. H. (1986), ‘Learning by augmenting rules and accumulating censors’, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approac. ., 45–61, Morgan Kaufmann, Los Altos, CA.Google Scholar
  57. Winston, P. H., Binford, T. O., Katz, B., and Lowry, M. (1983), ‘Learning physical descriptions from functional definitions, examples, and precedents’, in Proceedings AAAI-8., Washington, DC, pp. 433–439.Google Scholar
  58. Woolf, H. B. (1971), The Merriam- Webster Dictionar., Pocket Books, New York.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1988

Authors and Affiliations

  • Smadar Kedar-Cabelli
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityNew BrunswickUSA

Personalised recommendations