Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction
Page, Charles David, Jr.
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https://hdl.handle.net/2142/72095
Description
Title
Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction
Author(s)
Page, Charles David, Jr.
Issue Date
1993
Doctoral Committee Chair(s)
Frisch, Alan M.
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
Abstract
Unification is central to automated reasoning. Unification comes in a variety of forms, all of which compute (roughly stated) the greatest lower bound, or all maximal lower bounds, of any two or more syntactic objects in a partially-ordered set of such objects. The dual of unification is an operation called generalization, or anti-unification, which computes least or minimal upper bounds. As with unification, anti-unification comes in a variety of forms. The thesis of this dissertation is: anti-unification in its various forms is, like unification, a powerful tool for automated reasoning. In defense of the thesis, several forms of anti-unification in constraint logic, anti-unification relative to background information, are defined, and their semantic and computational properties are studied. It is shown that these forms of anti-unification are applicable to inductive logic programming (inductive learning of logic programs), speed-up learning, and knowledge base vivification (an approach to efficient deduction).
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