Possibilistic decision theory: from theoretical foundations to influence diagrams methodology

Le domaine de prise de décision est un domaine multidisciplinaire en relation avec plusieurs disciplines telles que l'économie, la recherche opérationnelle, etc. La théorie de l'utilité espérée a été proposée pour modéliser et résoudre les problèmes de décision. Ces théories ont été mises en cause par plusieurs paradoxes (Allais, Ellsberg) qui ont montré les limites de son applicabilité. Par ailleurs, le cadre probabiliste utilisé dans ces théories s'avère non approprié dans certaines situations particulières (ignorance totale, incertitude qualitative). Pour pallier ces limites, plusieurs travaux ont été élaborés concernant l'utilisation des intégrales de Choquet et de Sugeno comme critères de décision d'une part et l'utilisation d'une théorie d'incertitude autre que la théorie des probabilités pour la modélisation de l'incertitude d'une autre part. Notre idée principale est de profiter de ces deux directions de recherche afin de développer, dans le cadre de la décision séquentielle, des modèles de décision qui se basent sur les intégrales de Choquet comme critères de décision et sur la théorie des possibilités pour la représentation de l'incertitude. Notre objectif est de développer des modèles graphiques décisionnels, qui représentent des modèles compacts et simples pour la prise de décision dans un contexte possibiliste. Nous nous intéressons en particulier aux arbres de décision et aux diagrammes d'influence possibilistes et à leurs algorithmes d'évaluation.The field of decision making is a multidisciplinary field in relation with several disciplines such as economics, operations research, etc. Theory of expected utility has been proposed to model and solve decision problems. These theories have been questioned by several paradoxes (Allais, Ellsberg) who have shown the limits of its applicability. Moreover, the probabilistic framework used in these theories is not appropriate in particular situations (total ignorance, qualitative uncertainty). To overcome these limitations, several studies have been developed basing on the use of Choquet and Sugeno integrals as decision criteria and a non classical theory to model uncertainty. Our main idea is to use these two lines of research to develop, within the framework of sequential decision making, decision models based on Choquet integrals as decision criteria and possibility theory to represent uncertainty. Our goal is to develop graphical decision models that represent compact models for decision making when uncertainty is represented using possibility theory. We are particularly interested by possibilistic decision trees and influence diagrams and their evaluation algorithms

Solving multi-criteria decision problems under possibilistic uncertainty using optimistic and pessimistic utilities

International audienceThis paper proposes a qualitative approach to solve multi-criteria decision making problems under possibilistic uncertainty. De-pending on the decision maker attitude with respect to uncertainty (i.e. optimistic or pessimistic) and on her attitude with respect to criteria (i.e. conjunctive or disjunctive), four ex-ante and four ex-post decision rules are dened and investigated. In particular, their coherence w.r.t. the principle of monotonicity, that allows Dynamic Programming is studied

Possibilistic sequential decision making

International audienceWhen the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to the proposition a series of possibilistic criteria, namely: optimistic and pessimistic possibilistic qualitative criteria [7], possibilistic likely dominance [2] and [9], binary possibilistic utility [11] and possibilistic Choquet integrals [24]. This paper focuses on sequential decision making in possibilistic decision trees. It proposes a theoretical study on the complexity of the problem of finding an optimal strategy depending on the monotonicity property of the optimization criteria – when the criterion is transitive, this property indeed allows a polytime solving of the problem by Dynamic Programming. We show that most possibilistic decision criteria, but possibilistic Choquet integrals, satisfy monotonicity and that the corresponding optimization problems can be solved in polynomial time by Dynamic Programming. Concerning the possibilistic likely dominance criteria which is quasi-transitive but not fully transitive, we propose an extended version of Dynamic Programming which remains polynomial in the size of the decision tree. We also show that for the particular case of possibilistic Choquet integrals, the problem of finding an optimal strategy is NP-hard. It can be solved by a Branch and Bound algorithm. Experiments show that even not necessarily optimal, the strategies built by Dynamic Programming are generally very good

Order-of-Magnitude Influence Diagrams

In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams

New Graphical Model for Computing Optimistic Decisions in Possibility Theory Framework

This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic networks: the first one expresses agent's knowledge while the second one encodes agent's preferences representing a qualitative utility. We then propose an efficient algorithm for computing optimistic optimal decisions using our new model for representing possibilistic decision making under uncertainty. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph resulting from the fusion of agent's beliefs and preferences. This paper also proposes an alternative way for computing optimal optimistic decisions. The idea is to transform the two possibilistic networks into two equivalent possibilistic logic knowledge bases, one representing agent's knowledge and the other represents agent's preferences. We show that computing an optimal optimistic decision comes down to compute the inconsistency degree of the union of the two possibilistic bases augmented with a given decision

Lexicographic refinements in possibilistic decision trees and finite-horizon Markov decision processes

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Even though ap-pealing for its ability to handle qualitative decision problems, possibilisticdecision theory suffers from an important drawback. Qualitative possibilistic utility criteria compare acts through min and max operators, which leads to a drowning effect. To over-come this lack of decision power of the theory, several refinements have been proposed. Lexicographic refinements are particularly appealing since they allow to benefit from the Expected Utility background, while remaining qualitative. This article aims at extend-ing lexicographic refinements to sequential decision problems i.e., to possibilistic decision trees and possibilistic Markov decision processes, when the horizon is finite. We present two criteria that refine qualitative possibilistic utilities and provide dynamic programming algorithms for calculating lexicographically optimal policies

Decision Making under Uncertainty through Extending Influence Diagrams with Interval-valued Parameters

Influence Diagrams (IDs) are one of the most commonly used graphical and mathematical decision models for reasoning under uncertainty. In conventional IDs, both probabilities representing beliefs and utilities representing preferences of decision makers are precise point-valued parameters. However, it is usually difficult or even impossible to directly provide such parameters. In this paper, we extend conventional IDs to allow IDs with interval-valued parameters (IIDs), and develop a counterpart method of Copper’s evaluation method to evaluate IIDs. IIDs avoid the difficulties attached to the specification of precise parameters and provide the capability to model decision making processes in a situation that the precise parameters cannot be specified. The counterpart method to Copper’s evaluation method reduces the evaluation of IIDs into inference problems of IBNs. An algorithm based on the approximate inference of IBNs is proposed, extensive experiments are conducted. The experimental results indicate that the proposed algorithm can find the optimal strategies effectively in IIDs, and the interval-valued expected utilities obtained by proposed algorithm are contained in those obtained by exact evaluating algorithms

Collective decision making under qualitative possibilistic uncertainty: principles and characterization

Cette Thèse pose la question de la décision collective sous incertitude possibiliste. On propose différents règles de décision collective qualitative et on montre que dans un contexte possibiliste, l'utilisation d'une fonction d'agrégation collective pessimiste égalitariste ne souffre pas du problème du Timing Effect. On étend ensuite les travaux de Dubois et Prade (1995, 1998) relatifs à l'axiomatisation des règles de décision qualitatives (l'utilité pessimiste) au cadre de décision collective et montre que si la décision collective comme les décisions individuelles satisfont les axiomes de Dubois et Prade ainsi que certains axiomes relatifs à la décision collective, particulièrement l'axiome de Pareto unanimité, alors l'agrégation collective égalitariste s'impose. Le tableau est ensuite complété par une axiomatisation d'un pendant optimiste de cette règle de décision collective. Le système axiomatique que nous avons développé peut être vu comme un pendant ordinal du théorème de Harsanyi (1955). Ce résultat á été démontré selon un formalisme qui et basé sur le modèle de de Von NeuMann and Morgenstern (1948) et permet de comparer des loteries possibilistes. Par ailleurs, on propose une première tentative pour la caractérisation des règles de décision collectives qualitatives selon le formalisme de Savage (1972) qui offre une représentation des décisions par des actes au lieu des loteries. De point de vue algorithmique, on considère l'optimisation des stratégies dans les arbres de décision possibilistes en utilisant les critères de décision caractérisés dans la première partie de ce travail. On offre une adaptation de l'algorithme de Programmation Dynamique pour les critères monotones et on propose un algorithme de Programmation Multi-dynamique et un algorithme de Branch and Bound pour les critères qui ne satisfont pas la monotonie. Finalement, on établit une comparaison empirique des différents algorithmes proposés. On mesure les CPU temps d'exécution qui augmentent linéairement en fonction de la taille de l'arbre mais restent abordable même pour des grands arbres. Ensuite, nous étudions le pourcentage d'exactitude de l'approximation des algorithmes exacts par Programmation Dynamique: Il apparaît que pour le critère U-max ante l'approximation de l'algorithme de Programmation Multi-dynamique n'est pas bonne. Mais, ceci n'est pas si dramatique puisque cet algorithme est polynomial (et efficace dans la pratique). Cependant, pour la règle U+min ante l'approximation par Programmation Dynamique est bonne et on peut dire qu'il devrait être possible d'éviter une énumération complète par Branch and Bound pour obtenir les stratégies optimales.This Thesis raises the question of collective decision making under possibilistic uncertainty. We propose several collective qualitative decision rules and show that in the context of a possibilistic representation of uncertainty, the use of an egalitarian pessimistic collective utility function allows us to get rid of the Timing Effect. Making a step further, we prove that if both the agents' preferences and the collective ranking of the decisions satisfy Dubois and Prade's axioms (1995, 1998) and some additional axioms relative to collective choice, in particular Pareto unanimity, then the egalitarian collective aggregation is compulsory. The picture is then completed by the proposition and the characterization of an optimistic counterpart of this pessimistic decision rule. Our axiomatic system can be seen as an ordinal counterpart of Harsanyi's theorem (1955). We prove this result in a formalism that is based on Von NeuMann and Morgenstern framework (1948) and compares possibilisitc lotteries. Besides, we propose a first attempt to provide a characterization of collective qualitative decision rules in Savage's formalism; where decisions are represented by acts rather than by lotteries. From an algorithmic standpoint, we consider strategy optimization in possibilistic decision trees using the decision rules characterized in the first part of this work. So, we provide an adaptation of the Dynamic Programming algorithm for criteria that satisfy the property of monotonicity and propose a Multi-Dynamic programming and a Branch and Bound algorithm for those that are not monotonic. Finally, we provide an empirical comparison of the different algorithms proposed. We measure the execution CPU times that increases linearly according to the size of the tree and it remains affordable in average even for big trees. Then, we study the accuracy percentage of the approximation of the pertinent exact algorithms by Dynamic Programming: It appears that for U-max ante criterion the approximation of Multi-dynamic programming is not so good. Yet, this is not so dramatic since this algorithm is polynomial (and efficient in practice). However, for U+min ante decision rule the approximation by Dynamic Programming is good and we can say that it should be possible to avoid a full Branch and Bound enumeration to find optimal strategies