Learning from physical analogies: A study in analogy and the explanation process
Falkenhainer, Brian Carl
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https://hdl.handle.net/2142/21636
Description
Title
Learning from physical analogies: A study in analogy and the explanation process
Author(s)
Falkenhainer, Brian Carl
Issue Date
1989
Doctoral Committee Chair(s)
Forbus, Kenneth D.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Computer Science
Language
eng
Abstract
To make programs that understand and interact with the world as well as people do, we must duplicate the kind of flexibility people exhibit when conjecturing plausible explanations of the diverse physical phenomena they encounter. This process often involves drawing upon physical analogies--viewing the situation and its behavior as similar to familiar phenomena, conjecturing that they share analogous underlying causes, and using the plausible interpretation as a foothold to further understanding, analysis, and hypothesis refinement.
This thesis investigates analogical reasoning and learning applied to the task of constructing qualitative explanations for observed physical phenomena. Primary emphasis is placed on two central questions. First, how are analogies elaborated to sanction new inferences about a novel situation? This problem is addressed by contextual structure-mapping, a knowledge-intensive adaptation of Gentner's structure-mapping theory. It presents analogy elaboration as a map and analyze cycle, in which two situations are placed in correspondence, followed by problem solving and inference production focused on correspondence inadequacies. Second, how is the quality of a proposed analogy evaluated and used for some performance task? A theory of verification-based analogical learning is presented which addresses the tenuous nature of analogically inferred concepts and describes procedures that can be used to increase confidence in the inferred knowledge. Specifically, it relies on analogical inference to hypothesize new theories and simulation of those theories to analyze their validity. It represents a view of analogy as an iterative process of hypothesis formation, testing, and revision.
These ideas are illustrated via PHINEAS, a program which uses similarity to posit qualitative explanations for time-varying descriptions of physical behaviors. It builds upon existing work in qualitative physics to provide a rich environment in which to describe and reason with theories of the physical world.
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