摘要翻译：
在对等推理系统中，每个对等方可以在本地进行推理，但也可以请求一些熟人，这些熟人共享部分词汇。在本文中，我们考虑了对等推理系统，其中每个对等点的局部理论是定义在局部词汇表上的命题子句集。对等推理系统的一个重要特征是全局理论（所有对等理论的结合）是未知的（与基于划分的推理系统相反）。本文的主要贡献是提供了第一个在对等环境下的结果发现算法：DECA。它是随时的，从请求的对等点到越来越远的对等点逐渐计算结果。给出了对等推理系统熟人图的一个充分条件，保证了该算法的完备性。另一个重要贡献是通过Soughere Semantic peer-to-peer数据管理系统将这种通用的分布式推理设置应用到语义Web的设置中。本文的最后一个贡献是对我们提出的对等基础设施在1000个对等点的大型网络上的可扩展性进行了实验分析。
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英文标题：
《Distributed Reasoning in a Peer-to-Peer Setting: Application to the
  Semantic Web》
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作者：
P. Adjiman, P. Chatalic, F. Goasdoue, M. C. Rousset, L. Simon
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.
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PDF链接：
https://arxiv.org/pdf/1109.5716