Abstract

The ABC system repairs faulty Datalog theories using a combination of abduction, belief revision and conceptual change via reformation. Abduction and Belief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the language of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones. We apply an algorithm for solving Max-Sat problems, which we call the Partial Max-Sat algorithm, to form this Pareto front.

Highlights

  • We model the environment as a logical theory

  • We define: what we mean by a fault; the Datalog theories that ABC repairs; SL Resolution, which ABC uses for deduction; the repair operations it uses; and we illustrate the overproduction problem that this paper addresses

  • In order to show the generality of our techniques and avoid bias in the evaluation, these test examples were instead drawn from benchmark test and development sets used in research papers in a diverse range of areas of AI, including non-monotonic reasoning, belief revision, etc

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Summary

Introduction

We model the environment as a logical theory. The ABC system repairs faulty logical theories [9]. It is given a theory, T, as a set of axioms in the decidable logic Datalog [2], and some observations S, represented as a pair of sets of ground propositions. T is used to make predictions about the environment When these predictions conflict with the observations in S, the ABC system applies a sequence of repairs to T until it is fault free. Both incompatibility and insufficiency arise from reasoning failures: mismatches between the theorems of a theory T and the observations of the environment T (S), F(S). The true ground propositions are theorems of T and the false ones are not.

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