Connectionist approaches to inexact reasoning and learning systems for executive and decision support: Conceptual design

摘要

Connectionist approaches to the representation and reasoning about problems addressed by executive and decision support systems are described. It is shown that analog object structures coupled with learning produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Because a connectionist architecture is assumed, the prototype system is called the Connectionist Inexact Reasoning System or CIROS. Although uncharacteristic of neural architectures, an explanation capability is developed within CIROS and described herein. The primary contribution of CIROS is that it provides principles for design of inexact reasoning systems for managerial problem diagnosis.