Abstract
If an agent is to apply knowledge from its past experience to a presen episode, it must know what properties of the past situation can justifiably be projected onto the present on the basis of the known similarity between the situations. The problem of specifying when to generalize or reason by analogy, and when not to, therefore looms large for the designer of a learning system. One would like to be able to program into the system a set of criteria for rule formation from which the system can correctly generalize from data as they are received. Otherwise, all of the necessary rules the agent or system uses must be programmed in ahead of time, so that they are either explicitly represented in the knowledge base or derivable from it.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baker, M. and Burstein, M. H. (1987), ‘Implementing a model of human plausible reasoning’, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Los Altos, CA: Morgan Kaufmann, pp. 185–188.
Barwise, J. and Perry, J. (1983), Situations and Attitudes, Cambridge, MA: MIT Press.
Burstein, M. H. (1983), ‘A model of incremental analogical reasoning and debugging’, in Proceedings of the National Conference on Artificial Intelligence (AAAI-83), Los Altos, CA: Morgan Kaufmann, pp. 45–48.
Carbonell, J. G. (1983), ‘Derivational analogy and its role in problem solving’, in Proceedings of the National Conference on Artificial Intelligence (AAAI-83), Los Altos, CA: Morgan Kaufmann, pp. 64–69.
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 Approach, Volume II. Los Altos, CA: Morgan Kaufmann, pp. 371–392.
Carnap, R. (1963), Logical Foundations of Probability, Chicago: University of Chicago press.
Copi, I. M. (1972), Introduction to Logic, New York: The Macmillan Company.
Davies, T. (1985), Analogy, Informal Note No. IN-CSLI-85–4, Center for the Study of Language and Information, Stanford, CA.
Davies, T. R. and Russell, S. J. (1987), ‘A logical approach to reasoning by analogy’, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Los Altos, CA: Morgan Kaufmann, pp. 264–270.Also issued as Technical Note 385, Artificial Intelligence Center, SRI International, Menlo Park, CA, July 1987.
Genesereth, M. R. and Nilsson, N. J. (1987), Logical Foundations of Artificial Intelligence, Los Altos, CA: Morgan Kaufmann.
Gentner, D. (1983), ‘Structure mapping: A theoretical framework for analogy’, Cognitive Science 7: 155–170.
Georgeff, M. P. (1987), Many Agents Are Better Than One, Technical Note 417, Artificial Intelligence Center, SRI International, Menlo Park, CA.
Gick, M. L. and Holyoak, K. J. (1983), Schema induction and analogical transfer’, Cognitive Psychology 15: 1–38.
Goodman, L. A. and Kruskal, W. H. (1979), Measures of Association for Cross Classifications, New York: Springer-Verlag.
Goodman, N. (1983), Fact, Fiction, and Forecast, Cambridge, MA: Harvard University Press.
Greiner, R. (1985), Learning by Understanding Analogies, Technical Report STAN-CS8 5–1071, Stanford University, Stanford, CA.
Haberman, S. J. (1982), ‘Association, measures of’, in Kotz, S. and Johnson, N. L. (eds.), Encyclopedia of Statistical Science, Volume I, New York: John Wiley and Sons, pp. 130–137.
Hays, W. L. and Winkler, R. L. (1970), Statistics, Volume II: Probability, Inference, and Decision, San Francisco: Holt, Rinehart and Winston.
Hesse, M. B. (1966), Models and Analogies in Science, Notre Dame: University of Notre Dame Press.
Holland, J., Holyoak, K., Nisbett, R. and Thagard, P. (1986), Induction: Processes of Inference, Learning, and Discovery, Cambridge, MA: MIT Press.
Johnson, R. A. and Wichern, D. A. (1982), Applied Multivariate Statistical Analysis, Englewood Cliffs, NJ: Prentice-Hall.
Kedar-Cabelli, S. (1985), `Purpose-directed analogy’, in The Seventh Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 150–159.
Leblanc, H. (1969), ‘A rationale for analogical inference’, Philosophical Studies 20: 29–31.
Marr, D. (1982), Vision, New York: W. H. Freeman and Company.
Mill, J. S. (1900), A System of Logic, New York: Harper & Brothers Publishers.
Mitchell, T. M. (1980), The Need for Biases in Learning Generalizations, Technical Report CBM-TR-117, Rutgers University, New Brunswick, NJ.
Mitchell, T. M., Keller, R. M., and Kedar-Cabelli, S. T. (1986), ‘Explanation-based generalization: A unifying view’, Machine Learning 1: 47–80.
Montgomery, D. C. and Peck, E. A. (1982), Introduction to Linear Regression Analysis, New York: John Wiley & Sons.
Nilsson, N. (1984), Shakey the Robot, Technical Note 323, Intelligence Center, SRI International, Menlo Park, CA.
Nisbett, R. E., Krantz, D. H., Jepson, D., and Kunda, Z. (1983), ‘The use of statistical heuristics in everyday inductive reasoning’, Psychological Review 90: 339–363.
Rissland, E. L. and Ashley, K. D. (1986), ‘Hypotheticals as heuristic device’, in Proceedings of the National Conference on Artificial Intelligence (AAAI-86), Los Altos, CA: Morgan Kaufmann, pp. 289–297.
Rosenbloom, P. S. and Newell, A. (1986), ‘The chunking of goal hierarchies: A generalized model of practice’, in Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approach, Volume II. Los Altos, CA: Morgan Kaufmann, pp. 247–288.
Russell, S. J. (1986), Analogical and Inductive Inference, PhD Thesis, Stanford University, Stanford CA.
Russell, S. J. and Grosof, B. N. (1987), ‘A declarative approach to bias in inductive concept learning’, in Proceedings of the National Conference on Artificial Intelligence (AAAI-87), Los Altos, CA: Morgan Kaufmann, pp. 505–510.
Shaw, W. H. and Ashley, L. R. (1983), ‘Analogy and inference’, Dialogue: Canadian Journal of Philosophy 22: 415–432.
Subramanian, D. and Genesereth, M. R. (1987), ‘The relevance of irrelevance’, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Los Altos, CA: Morgan Kaufmann, pp. 416–422.
Thagard, P. and Nisbett, R. E. (1982), ‘Variability and confirmation’, Philosophical Studies 42, 379–394.
Theil, H. (1970), ‘On the estimation of relationships involving qualitative variables’, American Journal of Sociology 76: 103–154.
Ullman, J. D. (1983), Principles of Database Systems, Rockville, MD: Computer Science Press.
Vardi, M. Y. (1982), The Implication and Finite Implication Problems for Typed Template Dependencies, Technical Report STAN-CS-82–912, Stanford University, Stanford, CA.
Weitzenfeld, J. S. (1984), ‘Valid reasoning by analogy’, Philosophy of Science 51: 137–149.
Wilson, P. R. (1964), ‘On the argument by analogy’, Philosophy of Science 31: 34–39.
Winston, P. H. (1980) ‘Learning and reasoning by analogy’, Communications of the Association for Computing Machinery 23: 689–703.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Davies, T.R. (1988). Determination, Uniformity, and Relevance: Normative Criteria for Generalization and Reasoning by Analogy. In: Helman, D.H. (eds) Analogical Reasoning. Synthese Library, vol 197. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7811-0_11
Download citation
DOI: https://doi.org/10.1007/978-94-015-7811-0_11
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-8450-7
Online ISBN: 978-94-015-7811-0
eBook Packages: Springer Book Archive