Intelligent information fusion approach in cooperative multiagent systems

Benefits of, and needs for, developing multiagent systems could be easily noticed in solving problems that are unfeasibly solved by any individual agent. Such systems are expected to exempt intelligent collaboration and coordination behaviour. The challenge of having a fully cooperative agent society relies mainly on deriving a consensus decisions made by these autonomous, and goal directed entities. In many cases, these decisions encompass uncertainties caused by different design and environment factors. Thus, aggregating agents´ decisions with respect to their uncertainty factors is deemed to be the key to attain reliable delivery performance. In this article we present the use of an intelligent approach, the team consensus approach, to model the interaction and to bring team into a consensus. Agents engage in an uncertainty estimation stage whenever a group decision is required. This process allows each agent to assess its self-uncertainty and the conditional uncertainties of other agents. The process facilitates the computation of a weighting scheme that operates recursively on information collected by these agents until the group reaches a consensus. Whenever a new task is received, the uncertainty estimates of agents are updated and used to compute a new weighting scheme.