Analogy by Similarity

Part of the Synthese Library book series (SYLI, volume 197)


In this chapter l discuss the relative merits of the logical and similaritybased approaches to reasoning by analogy. Although recent work by Davies and the author has shown that, given appropriate background knowledge, analogy can be viewed as a logical inference process, I reach the conclusion that pure similarity can provide a probabilistic basis for inference, and that, under certain assumptions concerning the nature of representation, a quantitative theory can be developed for the probability that an analogy is correct as a function of the degree of similarity observed. This theory also accords with psychological data (Shepard), and together with the logical approach promises to form the basis for a general implementation of analogical reasoning.


Logical Approach Analogical Reasoning Inductive Logic Generalization Gradient 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, R. O. (1969), ‘Conclusive analogical argument’, Revue Internationale de Philosophie 23: 44–57.Google Scholar
  2. Carnap, Rudolf (1971), ‘A basic system of inductive logic, Part I’, in Carnap R. and Jeffrey, R. C. (eds.), Studies in Inductive Logic and Probability, Vol. I. Berkeley, CA: University of California Press.Google Scholar
  3. Davies, Todd (1985), Analogy. Informal Note No. IN-CSLI-85–4, Center for the Study of Language and Information, Stanford University.Google Scholar
  4. Davies, Todd and Russell, Stuart (1987), ‘A logical approach to reasoning by analogy’, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Milan, Italy.Google Scholar
  5. Dietterich, Thomas G. (1986), Learning at the Knowledge Level, Technical Report No. 86–30–1, Computer Science Department, Oregon State University.Google Scholar
  6. Greiner, Russell (1985), Learning by Understanding Analogies, PhD thesis. Technical Report no. STAN-CS-85–1071. Stanford University.Google Scholar
  7. Hesse, Mary (1966), Models and Analogies in Science, Notre Dame, Indiana: University of Notre Dame Press.Google Scholar
  8. Keynes, John Maynard (1957), A Treatise on Probability, London: Macmillan.Google Scholar
  9. Lenat, D., Prakash, M., and Shepherd, M. (1986), ‘CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks’, AI Magazine 6, No. 4.Google Scholar
  10. Mill, J. S. (1843), System of Logic, Book III, Ch. XX ‘Of Analogy’ in Vol. VIII of Collected Works of John Stuart Mill, University of Toronto Press; 1973.Google Scholar
  11. Nagel, Ernest (1961), The Structure of Science, New York: Harcourt, Brace and World.Google Scholar
  12. Ortony, A. (1979), ‘Role of similarity in similes and metaphors’, in Ortony, A. (ed.), Metaphor and Thought, Cambridge: Cambridge University Press.Google Scholar
  13. Russell, Stuart J. (1986a), ‘Preliminary steps toward the automation of induction’, in Proceedings of the National Conference on Artificial Intelligence, Philadelphia, PA: AAAI.Google Scholar
  14. Russell, Stuart J. (1986b), ‘A quantitative analysis of analogy by similarity’, in Proceedings of the National Conference on Artificial Intelligence, Philadelphia, PA: AAAI.Google Scholar
  15. Russell, Stuart J. (1986c), Analogical and Inductive Reasoning, PhD thesis, Stanford University.Google Scholar
  16. Shepard, R. N. (1958), ‘Stimulus and response generalization: Deduction of the generalization gradient from a trace model’, Psychological Review 65.Google Scholar
  17. Shepard, R. N. (1962), ‘The analysis of proximities: Multidimensional scaling with an unknown distance function (Parts I and II)’, Psychometrika 27.Google Scholar
  18. Shepard, Roger (1981), APA Division 3 Presidential Address, Los Angeles, August 25.Google Scholar
  19. Shepard, R. N. (1984), ‘Similarity and a law of universal generalization’. Paper presented at the annual meeting of the Psychonomic Society, San Antonio, TX.Google Scholar
  20. Tversky, Amos (1977), ‘Features of similarity’, Psychological Review 84, No. 4.Google Scholar
  21. Uemov, A. I. (1964), ‘The basic forms and rules of inference by analogy’, in Tavanec, P. (or Tavanets) (ed.), Problems of the Logic of Scientific Knowledge, Dordrecht: D. Reidel; 1970. Translated from the Russian by T. J. Blakeley; Moscow: Nauka.Google Scholar
  22. Utgoff, P. E. (1984), Shift of Bias for Inductive Concept Learning. PhD thesis, Rutgers University.Google Scholar
  23. Winston, Patrick H. (1978), ‘Learning by creating and justifying transfer frames’, Artificial Intelligence 10 No. 4.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1988

Authors and Affiliations

  1. 1.Computer Science DivisionUniversity of CaliforniaBerkeleyUSA

Personalised recommendations