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.