Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment: a case study

The issue of information sharing and exchanging is one of the most importan t issues in the areas of artificial intelligence and knowledge-based system s, or even in the broader areas of computer and information technology. Thi s paper deals with a special case of this issue by carrying out a case stud y of information sharing between two well-known heterogeneous uncertain rea soning models: the certainty factor model and the subjective Bayesian metho d. More precisely, this paper discovers a family of exactly isomorphic tran sformations between these two uncertain reasoning models. More interestingl y, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negat ive when performing such a transformation task. The direct motivation of th e investigation lies in a realistic consideration. In the past, expert syst ems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reas oning models are available. If there is a reasonable transformation mechani sm between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated s ystems are able to exchange and share useful information with each other, t hereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is signific ant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogene ous uncertain reasonings for their action selections and the information sh aring and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and proba bility theory. (C) 2001 Elsevier Science Inc. All rights reserved.