Representing Heuristic Knowledge in D-S Theory

TL;DR

This paper introduces a novel method for representing heuristic knowledge within Dempster-Shafer theory using evidential mappings based on mass functions, enhancing the handling of uncertain relationships.

Contribution

It proposes a new approach to encode heuristic knowledge in D-S theory through evidential mappings and details procedures for constructing these mappings from heuristic rules.

Findings

Evidential mappings relate to multi-valued and Bayesian models.

Procedures for constructing evidential mappings are detailed.

Belief propagation scenarios are discussed.

Abstract

The Dempster-Shafer theory of evidence has been used intensively to deal with uncertainty in knowledge-based systems. However the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major research problem. This paper presents an approach to representing such heuristic knowledge by evidential mappings which are defined on the basis of mass functions. The relationships between evidential mappings and multi valued mappings, as well as between evidential mappings and Bayesian multi- valued causal link models in Bayesian theory are discussed. Following this the detailed procedures for constructing evidential mappings for any set of heuristic rules are introduced. Several situations of belief propagation are discussed.