DISTRIBUTED REASONING WITH CONFLICTS IN A MULTI-CONTEXT FRAMEWORK
Multi-Context Systems (MCS) are logical formalizations of distributed context theories connected through a set of mapping rules, which enable information flow between different contexts. Reasoning in MCS introduces many challenges that arise from the heterogeneity of contexts with regard to the languages and inference systems that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. The current study describes a P2P rule-based reasoning model for MCS, which enables handling global inconsistency caused by the integration of mutually inconsistent context theories, by representing mappings as defeasible rules and performing some type of distributed defeasible reasoning. It also provides a distributed reasoning algorithm for query evaluation in MCS, which uses context information and an external preference relation (which e.g. may express trust information) to resolve the potential conflicts, analyzes its formal properties, and describes its use in an Ambient Intelligence use case scenario.
Distributed Intelligence Knowledge Representation Multi-Context Systems Contextual Reasoning Non-monotonic Reasoning
Antonis Bikakis Grigoris Antoniou
Institute of Computer Science,FO.R.T.H.,Vassilika Voutwn P.O.Box 1385,GR 71110,Heraklion,Greece
国际会议
2008高等智能国际会议(2008 International Conference on Advanced Intelligence)
北京
英文
2008-10-18（万方平台首次上网日期，不代表论文的发表时间）