Blackboard scheduler control knowledge for diagnostic problem-solving

Abstract

Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level diagnostic problem-solving behaviour. This paper makes use of a scheduler in order to achieve the ordering of strategies and addresses the important problem of how to make the scheduler more knowledge intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution approach described in this paper involves formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining and maintaining the knowledge of a classification expert system are applicable to the scheduler control knowledge. In this paper, the MINERVA expert system shell was extended by the addition of an explicit scheduler control level. The problem-solving cycle involves a deliberation phase, wherein all the heuristic classification strategies that are applicable are collected. This is followed by a scheduling phase wherein the classification expert system for scheduling automatically gathers evidence for and against each of the applicable strategic actions, thereby ranking them according to desirability. Finally, there is an action phase that executes the most highly ranked strategic task. One important innovation of this research is that of recursive heuristic classification: this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as a heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification: the classification alternatives that are reasoned about are dynamically generated in real-time and then evidence is gathered for and against these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of pre-enumerated fixed alternatives.