Fast-Division Architecture for Dempster-Shafer Belief Functions

A Sensor-Based Scheme for Activity Recognition in Smart Homes using Dempster-Shafer Theory of Evidence

This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using beta probability distribution function. The frames of opinion owners are derived automatically ...

A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition

We formulate Dempster Shafer Belief functions in terms of Propositional Logic, using the im­ plicit notion of provability underlying Demp­ ster Shafer Theory. The assignment of weights to the propositional literals enables the Belief functions to be explicitly computed using Net­ work Reliability techniques. Also, the updat­ ing of Belief functions using Dempster's Rule of Combination correspon...

Classic Works of the Dempster-Shafer Theory of Belief Functions

A Monte-Carlo Algorithm for Dempster-Shafer Belief

A very computationally-efficient MonteCarlo algorithm for the calculation of Dempster-Shafer belief is described. If Bel is the combination using Dempster’s Rule of belief functions Bel1, . . . ,Belm then, for subset b of the frame Θ, Bel(b) can be calculated in time linear in |Θ| and m (given that the weight of conflict is bounded). The algorithm can also be used to improve the complexity of t...

The Belief Calculus and Uncertain Reasoning

We formulate the Dempster-Shafer formalism of belief functions [Shafer 761 in the spirit of logical inference systems. Our formulation (called the belief calculus) explicitly avoids the use of set-theoretic notations. As such, it serves as an alternative for the use of the Dempster-Shafer formalism for uncertain reasoning.