Decision-Theoretic Control of Problem Solving: Principles and Architecture
This work addresses the problem of reflective control in AI systems for uncertain environments, presenting a foundational approach that is incremental in combining decision theory with architecture.
The paper tackles the design of autonomous real-time systems in uncertain environments by introducing decision-theoretic models for selecting problem-solving methods based on cost-benefit analysis and a computational architecture (Schemer-II) for resource-bounded problem solving, but does not report concrete numerical results.
This paper presents an approach to the design of autonomous, real-time systems operating in uncertain environments. We address issues of problem solving and reflective control of reasoning under uncertainty in terms of two fundamental elements: l) a set of decision-theoretic models for selecting among alternative problem-solving methods and 2) a general computational architecture for resource-bounded problem solving. The decisiontheoretic models provide a set of principles for choosing among alternative problem-solving methods based on their relative costs and benefits, where benefits are characterized in terms of the value of information provided by the output of a reasoning activity. The output may be an estimate of some uncertain quantity or a recommendation for action. The computational architecture, called Schemer-ll, provides for interleaving of and communication among various problem-solving subsystems. These subsystems provide alternative approaches to information gathering, belief refinement, solution construction, and solution execution. In particular, the architecture provides a mechanism for interrupting the subsystems in response to critical events. We provide a decision theoretic account for scheduling problem-solving elements and for critical-event-driven interruption of activities in an architecture such as Schemer-II.