Modifiable Combining Functions

TL;DR

Modifiable combining functions integrate two evidence combination approaches, providing a flexible tool for knowledge engineers to acquire, represent, explain, and modify evidence combinations in uncertain reasoning systems.

Contribution

They introduce a flexible, modifiable framework that combines existing evidence combination methods, aiding knowledge engineers in building adaptable reasoning systems.

Findings

Facilitate evidence acquisition and modification

Combine advantages of multiple approaches

Support explanation and representation of evidence

Abstract

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.