Adaptive Decision Support Systems via Problem Processor Learning

Abstract

In this chapter, we describe the potential advantages of developing adaptive decision support systems (adaptive DSSs) for the efficient and/or effective solution of problems in complex domains. The problem processing components of DSSs that subscribe to existing DSS paradigms typically utilize supervised learning strategies to acquire problem processing knowledge (PPK). On the other hand, the problem processor of an adaptive DSS utilizes unsupervised inductive learning, perhaps in addition to other forms of learning, to acquire some of the necessary PPK. Thus, adaptive DSSs are, to some extent, self-teaching systems with less reliance on external agents for PPK acquisition. To illustrate these notions, we examine an application in the domain concerned with the scheduling of jobs in flexible manufacturing systems (FMSs). We provide an architectural description for an adaptive DSS for supporting static scheduling decisions in FMSs and illustrate key problem processing features of the system using an example.