Decision-Theoretic Control of Problem Solving: Principles and Architecture
John S. Breese, Michael R. Fehling

TL;DR
This paper introduces a decision-theoretic framework and a computational architecture for autonomous systems to efficiently select and manage problem-solving methods under uncertainty, emphasizing resource-bounded reasoning and real-time decision making.
Contribution
It proposes a novel integration of decision-theoretic models with a flexible architecture for resource-bounded, real-time problem solving in uncertain environments.
Findings
Decision-theoretic models effectively guide method selection based on costs and benefits.
The Schemer-II architecture enables interleaving and communication among problem-solving subsystems.
The architecture supports interrupting activities in response to critical events.
Abstract
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…
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Taxonomy
TopicsComplex Systems and Decision Making · Cognitive Science and Mapping · AI-based Problem Solving and Planning
