Dimensions of Analogy

Analogy has been studied by many researchers in philosophy, psychology, and artificial intelligence. My aim in this chapter is to sketch the conceptual geography of computational research on analogy and to address some of the important issues that arise there. I shall try to identify the major dimensions along which various investigations of analogy differ. These chiefly concern how analogies are represented, how they are retrieved from memory, how they are exploited, and how learning takes place as the result of analogy. Particular attention will be paid to the different degrees to which different approaches along the different dimensions use syntactic, semantic, and pragmatic techniques. Important work on analogy has been done by philosophers such as Hesse (1966) and by psychologists, but for reasons that will be sketched in the next section, I shall concentrate here on computationa. approaches to analogy. The philosophical question of the role of analogies in inference will, however, be addressed.

[1]  Barr and Feigenbaum Edward A. Avron,et al.  The Handbook of Artificial Intelligence , 1981 .

[2]  J. Weitzenfeld.  Valid Reasoning by Analogy , 1984, Philosophy of Science.

[3]  Moshe Burstein,et al.  Concept formatio n by incremental analogica l reasoning and debugging , 1983 .

[4]  K. Holyoak,et al.  Surface and structural similarity in analogical transfer , 1987, Memory & cognition.

[5]  N. Cartwright.  How the laws of physics lie , 1984 .

[6]  D. Gentner.  Structure‐Mapping: A Theoretical Framework for Analogy* , 1983 .

[7]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[8]  P. Thagard,et al.  Computational Philosophy of Science , 1988 .

[9]  Ernest R. May,et al.  Thinking in Time: The Uses of History for Decision-Makers , 1986 .

[10]  R. Hall,et al.  Understanding analogical reasoning : viewpoints from psychology and related disciplines , 1986 .

[11]  C. Morris.  Foundations of the theory of signs , 1938 .

[12]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[13]  Paul Thagard,et al.  The Best Explanation: Criteria for Theory Choice , 1978 .

[14]  D. Gentner,et al.  Systematicity and Surface Similarity in the Development of Analogy. Technical Report No. 358. , 1985 .

[15]  Brian Falkenhainer,et al.  The Structure-Mapping Engine * , 2003 .

[16]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[17]  Mary Hesse,et al.  Models and analogies in science , 1970 .

[18]  Paul Thagard,et al.  A computational model of analogical problem solving , 1989 .

[19]  Patrick Henry Winston,et al.  Learning and reasoning by analogy , 1980, CACM.

[20]  Smadar T. Kedar-Cabelli,et al.  Analogy — From a Unified Perspective , 1988 .

[21]  Patrick Henry Winston,et al.  Learning New Principles from Precedents and Exercises , 1982, Artif. Intell..

[22]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[23]  John R. Anderson,et al.  Use of analogy in a production system architecture , 1989 .

[24]  Patrick Henry Winston,et al.  The psychology of computer vision , 1976, Pattern Recognit..

[25]  Rogers P. Hall.  Understanding analogical reasoning : computational approaches , 1986 .

[26]  John R. Anderson.  The Architecture of Cognition , 1983 .

[27]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[28]  Stuart J. Russell.  Analogy and Single-Instance Generalization , 1987 .