Distributed perception networks (DPN) are a MAS approach to large scale fusion of heterogeneous and noisy information. DPN agents can establish meaningful information filtering channels between the relevant information sources and the decision makers. Through specification of high level concepts, DPN agent organizations generate distributed Bayesian networks, which provide mappings between the observed symptoms and the hypotheses relevant to the decision making. In addition, DPNs support robust distributed inference as well as decentralized probabilistic resource allocation.
['Gregor Pavlin', 'P. de Oude', 'Jan Nunnink']
A MAS Approach to Fusion of Heterogeneous Information