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Multiagent Fusion Systems
Multiagent Fusion Systems
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Objective
Intelligent process control and decision making in complex systems require adequate situation assessment, which in turn requires processing of large amounts of heterogeneous information originating from different, spatially dispersed sources, such as, sensory systems, human observers, databases, etc. However, such fusion is not trivial, since it requires adequate mapping between very heterogeneous concepts, we are confronted with noisy information sources and, due to large amounts of information, significant processing resources might be required (i.e. computational bottlenecks). Another characteristic of the domains we are focusing on is that constellations of information sources can change frequently and, prior to the operation, we never know which information sources will be available. In addition, such fusion systems often provide results which have a critical impact on the decision making process and, consequently, further course of events. Therefore, high quality of fusion results and prevention of misleading results is indispensable.
In order to be able to deal with the mentioned challenges, we introduce Distributed Perception Networks (DPN), multi-agent systems which support fusion based on distributed Bayesian Networks (BN). In this context, our research is focused primarily on the following problems:
Research group members
Funding
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