Computational Antiterrorism Solutions Using a General-Purpose Evidence Fusion Engine
Patrick J. Talbot, Dennis R. Ellis · 2002
This paper describes our experience with the implementation of a generalpurpose evidence fusion engine for assimilating data and predicting intent in the mission area of antiterrorism. Data fusion is a difficult, multifaceted problem with many unsolved challenges, the major one being that the data fusion problem is ill posed—data fusion means many things to many people. Fusion of evidence provides a focused solution in cases where a decision maker is faced with discrete bits of information that are inherently uncertain. The DempsterShafer Belief Network algorithm is the basis for our approach to data fusion. Advantages are that it provides intuitive results; differentiates belief, ignorance, and disbelief; and resolves conflicts. Insights into the behavior of the belief network were obtained from an analytical study. Mathematical and empirical properties were explored and codified. Belief networks have been constructed for seven mission areas, leading to the characterization of the prototype testbed as a general-purpose data fusion engine. A novel feature of the evolving testbed is that the user is allowed to override the belief or disbelief associated with a hypothesis and the network will self-adjust, or learn, the appropriate link values by instantiating the override. The back-propagation algorithm from artificial neural network research is used to adjust the links. Although the work described here is in the mission area of antiterrorism, it has broad applicability to decision making in any circumstance where evidence is uncertain, incomplete, possibly conflicting, and arrives asynchronously over time.