FUNCTIONAL REPRESENTATION AND REASONING FOR REFLECTIVE SYSTEMS

Functional models have been extensively investigated in the context of several problem-solving task such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use str ucture-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the ac hievement of its goals. Then, we employ these models for performance-d riven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning, assign blame when it fails, and appropriately r edesign its knowledge and reasoning. This paper focuses on the model-b ased redesign of a path planner's task structure. It illustrates the m odel-based reflection using examples from an operational system called the Autognostic system.