Decision Making Using Probabilistic Inference Methods
This work addresses decision-making under uncertainty for AI systems, but it appears incremental as it builds on existing inference methods.
The paper tackles the problem of applying efficient probabilistic inference methods from expert systems to decision making under uncertainty, showing how these methods can be directly used and suggesting modifications to clustering algorithms to incorporate decision-making capabilities.
The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental normative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to decision problems. In addition to general approaches which need know nothing about the actual probabilistic inference method, we suggest some simple modifications to the clustering family of algorithms in order to efficiently incorporate decision making capabilities.