Modifiable Combining Functions
This work addresses the challenge of evidence combination for knowledge engineers building systems that reason under uncertainty, but it appears incremental as it synthesizes existing approaches rather than introducing a fundamentally new paradigm.
The paper tackles the problem of combining evidence in uncertain reasoning systems by introducing modifiable combining functions, which synthesize two common approaches to offer advantages while avoiding disadvantages. The result is a proposed tool for knowledge engineers that facilitates knowledge acquisition, representation, explanation, and modification in such systems.
Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.