Generating Explanations for Evidential Reasoning
This work addresses the need for interpretable explanations in evidential reasoning systems, but it appears incremental as it builds upon existing methods like Strat's approach.
The paper tackles the problem of generating explanations for evidential reasoning with belief functions in valuation-based systems by presenting two methods: a simplified sensitivity analysis approach and an evidence impact analysis based on information content, demonstrating their application in an evidential network.
In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is to examine the impact of evidence on the conclusion based on the measure of the information content in the evidence. We show the property of additivity for the pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network.