Toward Efficient Computation of the Dempster–Shafer Belief Theoretic Conditionals

Abstract

Dempster-Shafer (DS) belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. The recent work on the DS theoretic (DST) conditional approach, which is based on the Fagin-Halpern (FH) DST conditionals, appears to demonstrate the suitability of DS theory for incorporating both soft (generated by human-based sensors) and hard (generated by physics-based sources) evidence into the fusion process. However, the computation of the FH conditionals imposes a significant computational burden. One reason for this is the difficulty in identifying the FH conditional core, i.e., the set of propositions receiving nonzero support after conditioning. The conditional core theorem (CCT) in this paper redresses this shortcoming by explicitly identifying the conditional focal elements with no recourse to numerical computations, thereby providing a complete characterization of the conditional core. In addition, we derive explicit results to identify those conditioning propositions that may have generated a given conditional core. This "converse" to the CCT is of significant practical value for studying the sensitivity of the updated knowledge base with respect to the evidence received. Based on the CCT, we also develop an algorithm to efficiently compute the conditional masses (generated by FH conditionals), provide bounds on its computational complexity, and employ extensive simulations to analyze its behavior.

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