Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction

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).

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Page, Charles David, Jr.

Contributors dc:contributor

• Frisch, Alan M.

• Artificial Intelligence
• Computer Science

Identifier

(UMI)AAI9411741

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72095