Based on Lukasiewicz's three-valued logic,this paper builds a tri-ary uncertain reasoningmodel,and discusses its some basic properties which shows that it is consistent with our intu-itions. The characters of our ...Based on Lukasiewicz's three-valued logic,this paper builds a tri-ary uncertain reasoningmodel,and discusses its some basic properties which shows that it is consistent with our intu-itions. The characters of our model are as follows: 1)its measure for uncertainty is a distribution onthe true-value set of the multi-valued logic, 2)its propagation for uncertainties is related with theoperation of the logic,and 3) its parallel propagation follows a consertative展开更多
Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledg...Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.展开更多
文摘Based on Lukasiewicz's three-valued logic,this paper builds a tri-ary uncertain reasoningmodel,and discusses its some basic properties which shows that it is consistent with our intu-itions. The characters of our model are as follows: 1)its measure for uncertainty is a distribution onthe true-value set of the multi-valued logic, 2)its propagation for uncertainties is related with theoperation of the logic,and 3) its parallel propagation follows a consertative
基金Supported by the NSFC (No. 60772006, 60874105)the ZJNSF (Y1080422, R106745)Aviation Science Foundation (20070511001)
文摘Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.
