Reasoning with Inconsistencies in Propositional Peer-to-Peer Inference Systems

Philippe Chatalic 1, 2 Gia Hien Nguyen 1, 3 Marie-Christine Rousset 1, 3
1 GEMO - Integration of data and knowledge distributed over the web
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 IASI
LRI - Laboratoire de Recherche en Informatique
Abstract : n a peer-to-peer inference system, there is no centralized control or hierarchical organization: each peer is equivalent in functionality and cooperates with other peers in order to solve a collective reasoning task. Since peer theories model possibly different viewpoints, even if each local theory is consistent, the global theory may be inconsistent. We exhibit a distributed algorithm detecting inconsistencies in a fully decentralized setting. We provide a fully distributed reasoning algorithm, which computes only well-founded consequences of a formula, i.e., with a consistent set of support.
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Communication dans un congrès
European Conference on Artificial Intelligence (ECAI'06), Aug 2006, Riva del Garda, Italy. IOS Press, 2006
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Dernière modification le : jeudi 11 octobre 2018 - 08:48:01
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Philippe Chatalic, Gia Hien Nguyen, Marie-Christine Rousset. Reasoning with Inconsistencies in Propositional Peer-to-Peer Inference Systems. European Conference on Artificial Intelligence (ECAI'06), Aug 2006, Riva del Garda, Italy. IOS Press, 2006. 〈inria-00179862〉

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