Aggregation in multiagent systems and the problem of truth-tracking

One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially con-flicting sources of information. Examples are multi-sensor fusion, database integration and expert systems develop-ment. In this paper we are interested in the aggregation of propositional logic-based information, a problem recently addressed in the literature on information fusion. It has ap-plications in multi-agent systems that aim at aggregating the distributed agent-based knowledge into an (ideally) unique set of propositions. We consider a group of autonomous agents who individually hold a logically consistent set of propositions. Each set of propositions represents an agent’s beliefs on issues on which the group has to make a collective decision. To make the collective decision, several aggrega-tion procedures have been proposed in the literature. As-suming that all propositions in question are factually right or wrong, we ask how good belief fusion is as a truth tracker. Will it single out the true set of propositions? And how does information fusion compare with other aggregation proce-dures? We address these questions in a probabilistic frame-work and show that information fusion does especially well for agents with a middling competence of hitting the truth of an individual proposition