Strategy Selection in Influence Diagrams using Imprecise Probabilities

TL;DR

This paper introduces a new algorithm for decision making in influence diagrams using imprecise probabilities, capable of finding global optimal strategies and providing approximate solutions with error guarantees.

Contribution

It presents a novel approach that integrates imprecise probabilities into influence diagram decision strategies, including a global maximum method and an anytime approximation.

Findings

The algorithm effectively finds global optimal strategies.

Imprecise probabilities are handled straightforwardly.

Experiments demonstrate practical applicability and efficiency.

Abstract

This paper describes a new algorithm to solve the decision making problem in Influence Diagrams based on algorithms for credal networks. Decision nodes are associated to imprecise probability distributions and a reformulation is introduced that finds the global maximum strategy with respect to the expected utility. We work with Limited Memory Influence Diagrams, which generalize most Influence Diagram proposals and handle simultaneous decisions. Besides the global optimum method, we explore an anytime approximate solution with a guaranteed maximum error and show that imprecise probabilities are handled in a straightforward way. Complexity issues and experiments with random diagrams and an effects-based military planning problem are discussed.