Adaptive Decision Support Systems via Problem Processor Learning

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