A modular approach to adaptive Bayesian information fusion

In this paper we show that causal probabilistic models can facilitate the design of robust and flexible fusion systems. Observed events resulting from stochastic causal processes can be modeled with the help of causal Bayesian networks, mathematically rigorous and compact probabilistic causal models. Bayesian networks explicitly represent conditional independence which facilitates decentralized modeling and information fusion. Starting with the theory of BNs we derive design and organization rules for distributed multi-agent systems that implement exact belief propagation without centralized configuration and fusion control. In this way we can design multi-agent fusion systems which can adapt to rapidly changing information source constellations and can efficiently process large quantities of information.