摘要:

Knowledge base validation and knowledge base refinement aim to help the expert to improve an existing knowledge base. They deal with the final knowledge acquisition phase and rely on a quality measurement of an existing knowledge base. We present our approach to knowledge base refinement, which is based on results in the domain of knowledge base validation. Our approach is based on a general consistency definition of a knowledge base and on a study of causes of knowledge base inconsistency. Our approach relies significantly on a differentiation of sure and expert knowledge in the knowledge base. We have implemented a system that has two phases: one computational phase decides on the consistency of a knowledge base, and, if necessary, a second phase helps the expert to interactively update the knowledge base. We present some related work in the domain. We illustrate the use of our system with an example.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00275.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

An uncertainty reasoning method is presented in this article. The method can be used to compute from a given set of conditional probabilities the best lower bounds and the best upper bounds of those conditional probabilities that are not explicitly provided. The computation of the best upper(lower) bound of such a conditional probability relies on solution of a linear programming problem. Some reduction techniques are proposed in this article to improve the efficiency of our uncertainty reasoning method. As illustrated in Section 4.3, for many uncertainty reasoning problems in medical diagnosis, by using our reduction techniques, the best range of a conditional probability, which is specified by a lower bound and an upper bound, can be computed in polynomial time in terms of the number of basic events involved in the reasoning.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00276.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

This article studies finite size networks that consist of interconnections of synchronously evolving processors. Each processor updates its state by applying an activation function to a linear combination of the previous states of all units. We prove thatanyfunction for which the left and right limits exist and are different can be applied to the neurons to yield a network which is at least as strong computationally as a finite automaton. We conclude that if this is the power required, one may choose any of the aforementioned neurons, according to the hardware available or the learning software preferred for the particular application.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00277.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00274.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY