2015/04/25 by Kedian Mu, Mu, Kedian, Kewen Wang +3
Computer Science · #68T30 #Advanced Algebra and Logic #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #I.2.4 #Logic, Reasoning, and Knowledge #Machine Learning and Algorithms #acm:68T30 #cs.AI #msc:68T30
paper · pdf · doi:10.48550/arxiv.1504.06700
arxiv created 2015/04/25 · openalex publication_date 2015/04/25 · arxiv updated 2015/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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≤-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.