Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

Abstract

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 peerto-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.

Distributed Defeasible Speculative Reasoning in Ambient Environment

“…Agents in the framework maintain their own sets of beliefs, information about the environment and the default hypotheses, and has embedded a defeasible reasoning engine executing the inference procedures. The implementation also covers the distributed reasoning approach described in [27].…”

SomeWhere: A Scalable Peer-to-Peer Infrastructure for Querying Distributed Ontologies

“…In SomeWhere the query rewriting problem can be reduced to a consequence finding problem in distributed propositional theories. It is performed by a message-passing algorithm named DeCA: Decentralized Consequence finding Algorithm [3]. As a result, query answering in SomeWhere is sound, complete and terminates.…”

“…We have shown in [3] that the maximal conjunctive rewritings of a query q in SomeWhere are the negation of the prime proper implicates of ¬q w.r.t the propositional encoding of the schema. We use the distributed message-passing DeCA algorithm [3] to compute them.…”

“…We have shown that query answering in SomeRDFS can be achieved using a rewrite and evaluate strategy, and that the corresponding rewriting problem can be reduced to the same consequence finding problem in distributed propositional theories as in [3]. Moreover, the consequence finding problem resulting from the propositional encoding of the fragment of RDFS that we consider is tractable since the resulting propositional theories are reduced to clauses of length 2 for which the reasoning problem is in P. The experiments reported in [4] show that it takes in mean 0.07s to SomeWhere for a complete reasoning on randomly generated sets of clauses of length 2 distributed on 1000 peers.…”

Automated Verification of Resource Requirements in Multi-Agent Systems Using Abstraction

“…While the use of pre-defined plans makes it easier to guarantee the behaviour of the multi-agent system, e.g., [2], it can make it harder for the system to solve novel problems not anticipated by the system designers. As a result, there has recently been increasing interest in providing general reasoning capabilities to agents in multi-agent systems (see, for example, [3,4]). However, while the incorporation of reasoning abilities into agents brings great benefits in terms of flexibility and ease of development, these approaches also raise new challenges for the agent developer, namely, how to ensure correctness (will an agent produce the correct output for all legal inputs), termination (will an agent produce an output at all), and response time (how much computation will an agent have to do before it generates an output).…”