Trustworthy situation assessment via belief networks

A probabilistic approach to truth maintenance is presented, specifically geared for automated problem solvers based on Bayesian belief network (BN) technology. Nodes and links in a BN capture semantic relationships among various domain related concepts. In the absence of firmer knowledge, default assumptions provide the beliefs for some nodes in the BN. Before posting incoming evidence into a BN node, a truth maintenance procedure is invoked to check for information consistency between the node´s current expected state and the new observed state. In case of inconsistency, the truth maintenance procedure revises some default assumptions, by isolating those nodes causing inconsistency, via a sensitivity analysis procedure that exploits the strengths of BN causal dependency. The approach is specifically targeted for trustworthy situation assessment in the context of a military stability and support operation (SASO) scenario.