Preferential Multi-Context Systems

April 25, 2015 Β· Declared Dead Β· πŸ› International Journal of Approximate Reasoning

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Authors Kedian Mu, Kewen Wang, Lian Wen arXiv ID 1504.06700 Category cs.AI: Artificial Intelligence Citations 8 Venue International Journal of Approximate Reasoning Last Checked 4 months ago
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.
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