Modifiable Combining Functions
Paul Cohen, Glenn Shafer, Prakash P. Shenoy

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.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Bayesian Modeling and Causal Inference · Logic, Reasoning, and Knowledge
