3 research outputs found
Generating Explanations for Evidential Reasoning
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.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Corporate Evidential Decision Making in Performance Prediction Domains
Performance prediction or forecasting sporting outcomes involves a great deal
of insight into the particular area one is dealing with, and a considerable
amount of intuition about the factors that bear on such outcomes and
performances. The mathematical Theory of Evidence offers representation
formalisms which grant experts a high degree of freedom when expressing their
subjective beliefs in the context of decision-making situations like
performance prediction. Furthermore, this reasoning framework incorporates a
powerful mechanism to systematically pool the decisions made by individual
subject matter experts. The idea behind such a combination of knowledge is to
improve the competence (quality) of the overall decision-making process. This
paper reports on a performance prediction experiment carried out during the
European Football Championship in 1996. Relying on the knowledge of four
predictors, Evidence Theory was used to forecast the final scores of all 31
matches. The results of this empirical study are very encouraging.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
Generating Explanations for Evidential Reasoning
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. 1 Introduction The developers of expert systems have realized that a good facility to explain the computer-based reasoning to users is a prerequisite to their more widespread acceptance. The importance of explanation is due to two reasons. First, expert systems are usually used to solve difficult problems. A good explanation facility a..