Explanation-based theory revision: An approach to the problems of incomplete and incorrect theories
Rajamoney, Shankar Anandsubramaniam
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https://hdl.handle.net/2142/22579
Description
Title
Explanation-based theory revision: An approach to the problems of incomplete and incorrect theories
Author(s)
Rajamoney, Shankar Anandsubramaniam
Issue Date
1989
Doctoral Committee Chair(s)
DeJong, Gerald F.
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
It is increasingly apparent that knowledge is essential for intelligent behavior. This has led to a new trend in Artificial Intelligence towards knowledge-intensive methods such as explanation-based learning, qualitative reasoning, theory-driven machine discovery and deep-model expert systems. Knowledge-intensive systems rely on a model of the domain, called a domain theory, to fulfill their tasks. A domain theory consists of an encoding of the knowledge required by the system to draw inferences about situations of interest. The primary shortcomings of a system that relies on a domain theory are twofold: (1) The construction of an adequately complete and correct domain theory to insure a satisfactory level of performance is virtually impossible for most complex real-world domains. (2) The performance of the system is fundamentally limited by the deductive closure of the knowledge initially encoded into its domain theory and consequently the system does not perform well in underspecified or dynamically changing domains.
This thesis describes a method called explanation-based theory revision for augmenting and correcting a domain theory. The method consists of detecting failures due to the inadequacies of the domain theory, hypothesizing modifications or additions to the domain theory to eliminate the failures, designing experiments to refute incorrect hypotheses, recalling previous experiences of the system to eliminate inconsistent hypotheses and selecting a best theory from the remaining theories based on aesthetic criteria. Explanation-based theory revision addresses both the problems mentioned above. It corrects and augments inadequate domain theories when failures occur. Furthermore since the deductive closure of the knowledge in the domain theory changes the system learns at the knowledge level. Explanation-based theory revision has been incorporated into a system called COAST. COAST revises qualitative theories of the physical world. COAST has been demonstrated on a number of implemented examples involving the revision of qualitative theories about physical phenomena such as evaporation, osmosis, flow of fluids, dissolving of substances, chemical decomposition of compounds and combustion.
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