An Abductive Framework For Computing Knowledge Base Updates

TL;DR

This paper presents a unified abductive framework for updating knowledge bases represented by extended disjunctive programs, enabling various update types and consistency restoration through logic programming answer sets.

Contribution

It introduces a transformation from abductive to update programs and generalizes abduction for knowledge base updates within a uniform logic programming framework.

Findings

Provides a uniform framework for different knowledge base updates.

Characterizes view and theory updates using abductive programs.

Analyzes computational complexity of update tasks.

Abstract

This paper introduces an abductive framework for updating knowledge bases represented by extended disjunctive programs. We first provide a simple transformation from abductive programs to update programs which are logic programs specifying changes on abductive hypotheses. Then, extended abduction, which was introduced by the same authors as a generalization of traditional abduction, is computed by the answer sets of update programs. Next, different types of updates, view updates and theory updates are characterized by abductive programs and computed by update programs. The task of consistency restoration is also realized as special cases of these updates. Each update problem is comparatively assessed from the computational complexity viewpoint. The result of this paper provides a uniform framework for different types of knowledge base updates, and each update is computed using existing procedures of logic programming.