Solving abduction by computing joint explanations

Abstract

An extension of abduction is investigated where explanations are jointly computed by sets of interacting agents. On the one hand, agents are allowed to partially contribute to the reasoning task, so that joint explanations can be singled out even if each agent does not have enough knowledge for carrying out abduction on its own. On the other hand, agents maintain their autonomy in choosing explanations, each one being equipped with a weighting function reflecting its perception about the reliability of sets of hypotheses. Given that different agents may have different and possibly contrasting preferences on the hypotheses to be chosen, some reasonable notions of agents' agreement are introduced, and their computational properties are thoroughly studied. As an example application of the framework discussed in the paper, it is shown how to handle data management issues in Peer-to-Peer systems and, specifically, how to provide a repair-based semantics to inconsistent ones.

Computing Abductive Explanations

“…The concept of simplicity adopted in this paper is based on minimality with respect to set inclusion. In [18] an extension of abduction where explanations are jointly computed by sets of interacting agents is investigated. Also in this paper only the propositional case is analyzed and the use of answer set engines such as DLV.…”

Complete Distributed Consequence Finding With Message Passing

“…Since abduction in clausal theories can be implemented with consequence finding, such work is somehow related to distributed consequence finding. Greco (Greco, 2007) considers how to build joint explanations from multiple agents in a P2P setting like (Adjiman et al, 2005), but incorporates preference handling to have an agreement between agents. By extending a blackboard architecture of (Ciampolini et al, 2003), Ma et al (Ma et al, 2008) address distribution of abductive logic programming agents by allowing agents to enter and exit proofs done by other agents.…”