A comparison of two approaches for solving unconstrained influence diagrams

Abstract

Influence diagrams and decision trees represent the two most common frameworks for specifying and solving decision problems. As modeling languages, both of these frameworks require that the decision analyst specifies all possible sequences of observations and decisions (in influence diagrams, this requirement corresponds to the constraint that the decisions should be temporarily linearly ordered). Recently the unconstrained influence diagram was proposed to address this drawback. In this framework, we may have a partial ordering of the decisions, and a solution to the decision problem therefore consists not only of a decision policy for the various decisions, but also of a conditional specification of what to do next. Relative to the complexity of solving an influence diagram, finding a solution to an unconstrained influence diagram may be computationally very demanding w.r.t. both time and space. Hence, there is a need for efficient algorithms that can deal with (and take advantage of) the idiosyncrasies of the language. In this paper we propose two such solution algorithms. One resembles the variable elimination technique from influence diagrams, whereas the other is based on conditioning and supports any-space inference. Finally we present an empirical comparison of the proposed methods.

Decision analysis networks

“…Later another DAN was built for deciding when to implant a knee prosthesis [23]. These medical networks are available at www.ProbModelXML.org/networks, together with other DANs for the problems proposed previously in 7 UIDs are a partial exception to this assertion: they were implemented in Elvira [11], [20], [21], [22] with the goal of measuring the efficiency of some inference algorithms, but Elvira's graphical user interface for building UIDs is not mature, and no document explains how to build UIDs in Elvira. [2] yes yes yes…”

Probabilistic decision graphs for optimization under uncertainty

“…If, namely, D has been decided, performing a test will only be a cost and should be skipped, and the decision on skipping a test can just as well be taken before the decision on D is taken. Actually, this kind of reasoning can be performed algorithmically on the basis of a set of rules for analyzing UIDs (Ahlmann-Ohlsen et al 2009). …”