Representing Heuristic Knowledge in D-S Theory
This addresses a key limitation in knowledge-based systems for AI researchers, though it appears incremental as it builds on existing theory.
The paper tackles the problem of representing uncertain heuristic knowledge in Dempster-Shafer theory by introducing evidential mappings based on mass functions, and it provides procedures for constructing these mappings from heuristic rules.
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.