Back to Search Start Over

A Generalisation of AGM Contraction and Revision to Fragments of First-Order Logic.

Authors :
Zhiqiang Zhuang
Zhe Wang
Wang, Kewen
Delgrande, James
Source :
Journal of Artificial Intelligence Research; 2019, Vol. 64, p147-179, 33p
Publication Year :
2019

Abstract

AGM contraction and revision assume an underlying logic that contains propositional logic. Consequently, this assumption excludes many useful logics such as the Horn fragment of propositional logic and most description logics. Our goal in this paper is to generalise AGM contraction and revision to (near-)arbitrary fragments of classical first-order logic. To this end, we first define a very general logic that captures these fragments. In so doing, we make the modest assumptions that a logic contains conjunction and that information is expressed by closed formulas or sentences. The resulting logic is called first-order conjunctive logic or FC logic for short. We then take as the point of departure the AGM approach of constructing contraction functions through epistemic entrenchment, that is the entrenchment-based contraction. We redefine entrenchment-based contraction in ways that apply to any FC logic, which we call FC contraction. We prove a representation theorem showing its compliance with all the AGM contraction postulates except for the controversial recovery postulate. We also give methods for constructing revision functions through epistemic entrenchment which we call FC revision; which also apply to any FC logic. We show that if the underlying FC logic contains tautologies then FC revision complies with all the AGM revision postulates. Finally, in the context of FC logic, we provide three methods for generating revision functions via a variant of the Levi Identity, which we call contraction, withdrawal and cut generated revision, and explore the notion of revision equivalence. We show that withdrawal and cut generated revision coincide with FC revision and so does contraction generated revision under a finiteness condition. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10769757
Volume :
64
Database :
Supplemental Index
Journal :
Journal of Artificial Intelligence Research
Publication Type :
Academic Journal
Accession number :
145749039
Full Text :
https://doi.org/10.1613/jair.1.11337