Preferential Multi-Context Systems

TL;DR

This paper introduces preferential multi-context systems (PMCS), a framework that organizes contexts by preference levels, restricting information flow to improve reasoning and analyze inconsistency in heterogeneous knowledge systems.

Contribution

It proposes the PMCS framework with a total preorder over contexts, defining $l$-sections and $l_{\leq}$-equilibria, and explores their semantics, inconsistency analysis, and computational complexity.

Findings

Defined $l$-sections capturing preferred context groups.

Extended equilibrium semantics to $l_{\leq}$-equilibria.

Analyzed inconsistency and complexity issues in PMCS.

Abstract

Multi-context systems (MCS) presented by Brewka and Eiter can be considered as a promising way to interlink decentralized and heterogeneous knowledge contexts. In this paper, we propose preferential multi-context systems (PMCS), which provide a framework for incorporating a total preorder relation over contexts in a multi-context system. In a given PMCS, its contexts are divided into several parts according to the total preorder relation over them, moreover, only information flows from a context to ones of the same part or less preferred parts are allowed to occur. As such, the first $l$ preferred parts of an PMCS always fully capture the information exchange between contexts of these parts, and then compose another meaningful PMCS, termed the $l$-section of that PMCS. We generalize the equilibrium semantics for an MCS to the (maximal) $l_{\leq}$-equilibrium which represents belief states at least acceptable for the $l$-section of an PMCS. We also investigate inconsistency analysis in PMCS and related computational complexity issues.