Unconstrained Influence Diagrams

TL;DR

This paper extends influence diagrams to handle decision scenarios with flexible orderings, introducing GS-DAGs to represent and determine optimal strategies in such complex, evidence-dependent decision processes.

Contribution

It introduces GS-DAGs, a novel DAG structure for representing optimal step-strategies in unconstrained influence diagrams, along with a construction method and analysis techniques.

Findings

GS-DAGs effectively model flexible decision sequences.

Method for constructing GS-DAGs from influence diagrams.

Analysis of relevant past reduces GS-DAG complexity.

Abstract

We extend the language of influence diagrams to cope with decision scenarios where the order of decisions and observations is not determined. As the ordering of decisions is dependent on the evidence, a step-strategy of such a scenario is a sequence of dependent choices of the next action. A strategy is a step-strategy together with selection functions for decision actions. The structure of a step-strategy can be represented as a DAG with nodes labeled with action variables. We introduce the concept of GS-DAG: a DAG incorporating an optimal step-strategy for any instantiation. We give a method for constructing GS-DAGs, and we show how to use a GS-DAG for determining an optimal strategy. Finally we discuss how analysis of relevant past can be used to reduce the size of the GS-DAG.