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Information Sharing between Heterogeneous Uncertain Reasoning Models in a Multi-Agent Environment: A Case Study

By Xudong Luo, Chengqi Zhang and Ho-fung Leung


The issue of information sharing and exchanging is one of the most important issues in the areas of arti cial intelligence and knowledge based systems, or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, dierent ones can handle dierent degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is signi cant in the research area of multi-agent systems because dierent agents in a multi-agent system could employ dierent expert The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, C..

Year: 2001
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