Decision Making Using Probabilistic Inference Methods

Abstract

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 nonnative 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. 1986).,

Optimal control as a graphical model inference problem

“…There is a line of research on how to compute optimal action sequences in influence diagrams using the idea of probabilistic inference (Cooper 1988;Tatman and Shachter 1990;Shachter and Peot 1992). Although this technique can be implemented efficiently using the junction tree approach for single decisions, the approach does not generalize in an efficient way to optimal decisions, in the expected-reward sense, in multi-step tasks.…”

“…For a Markov decision process (MDP) there is an efficient solution in terms of the Bellman equation. 1 For a general influence diagram, the marginalization approach as proposed in Cooper (1988), Tatman and Shachter (1990), Shachter and Peot (1992) will result in an intractable optimization problem over u 0:T −1 that cannot be solved efficiently (using dynamic programming), unless the influence diagram has an MDP structure.…”

Normative engineering risk management systems

“…Recent research that extends message-passing inference to decision problems (Shachter and Peot 1992) Nonnative system scaleup to handle a problem of realistic complexity presents domain-dependent (e.g., modeling gas leak evolution) and general challenges. Most importantly, nonmyopic information gathering is exponentially complex and is highly dependent on the complexity of the diagnosis, dynamic evolution, and decision-making models.…”

Explanation of Bayesian Networks and Influence Diagrams in Elvira

“…In order to avoid confusions, we must mention that the meaning of evidence in Elvira is very different from its meaning in some methods oriented to the computation of the value of information in IDs, such as [36], [37], [38]. For those methods, the introduction of evidence e leads to a different decision problem in which the values of the variables in E would be known with certainty before making any decision.…”