A Monte-Carlo Algorithm for Dempster-Shafer Belief
This provides a faster method for uncertainty reasoning in AI and decision-making systems, though it appears incremental as it builds on existing Dempster-Shafer theory.
The paper tackles the computational inefficiency of calculating Dempster-Shafer belief by introducing a Monte-Carlo algorithm that computes belief in linear time relative to input size, given bounded conflict weight, and extends to improve Markov tree algorithms and other logics.
A very computationally-efficient Monte-Carlo algorithm for the calculation of Dempster-Shafer belief is described. If Bel is the combination using Dempster's Rule of belief functions Bel, ..., Bel,7, then, for subset b of the frame C), Bel(b) can be calculated in time linear in 1(31 and m (given that the weight of conflict is bounded). The algorithm can also be used to improve the complexity of the Shenoy-Shafer algorithms on Markov trees, and be generalised to calculate Dempster-Shafer Belief over other logics.