Lexicographically-ordered constraint satisfaction problems

We describe a simple CSP formalism for handling multi-attribute preference problems with hard constraints, one that combines hard constraints and preferences so the two are easily distinguished conceptually and for purposes of problem solving. Preferences are represented as a lexicographic order over complete assignments based on variable importance and rankings of values in each domain. Feasibility constraints are treated in the usual manner. Since the preference representation is ordinal in character, these problems can be solved with algorithms that do not require evaluations to be represented explicitly. This includes ordinary CSP algorithms, although these cannot stop searching until all solutions have been checked, with the important exception of heuristics that follow the preference order (lexical variable and value ordering). We describe relations between lexicographic CSPs and more general soft constraint formalisms and show how a full lexicographic ordering can be expressed in the latter. We discuss relations with (T)CP-nets, highlighting the advantages of the present formulation, and we discuss the use of lexicographic ordering in multiobjective optimisation. We also consider strengths and limitations of this form of representation with respect to expressiveness and usability. We then show how the simple structure of lexicographic CSPs can support specialised algorithms: a branch and bound algorithm with an implicit cost function, and an iterative algorithm that obtains optimal values for successive variables in the importance ordering, both of which can be combined with appropriate variable ordering heuristics to improve performance. We show experimentally that with these procedures a variety of problems can be solved efficiently, including some for which the basic lexically ordered search is infeasible in practice

Configuration interactive et contraintes : connaissances, filtrage et extensions

The value of our research work is rooted in the following observations :-1- the life cycle of products, systems, services and processes is tending to get shorter ; -2- new designs and updates of products on the market are becoming more and more frequent, leading to increasingly short design cycles ; -3 technologies are constantly changing, requiring permanent, ongoing acquisition of knowledge ; -4-the diversity of products offered on the market is growing all the time, ranging from customizable or configurable to made-to-measure or designed to order.These trends, and the mass of information and knowledge that requires treating as a result of them, are placing heavy demands on designers, requiring ever more attentiveness and increasingly intense cognitive effort. The result is an increased risk that the product does not fully meet the customer’s needs, that it is difficult to implement or manufacture, or that it will be prohibitively expensive. The aim of our work is thus to help the design process to reduce these risks and errors by delivering software tools and methodological environments that serve to capitalize and exploit general, contextual, academic, expert or business knowledge.Our work on various complex industrial cases has led us to take into consideration two kinds of knowledge, involving on the one hand the "product domain" and on the other the "product diversity element". Each kind of knowledge leads to differing industrial cases. The first kind of knowledge encompasses the scientific and technical aspects, but also the specific rules governing the business in question. This knowledge is required in order to define the product itself, and involves issues that can be resolved by aiding the product /system/service design. The second kind of knowledge relates to the diverse nature of the products, and involves issues of customization or configuration of the product/system/service.Our aim is to help in what might be called "routine" design, where different kinds and various types of knowledge exist, due to the recurrent nature of the activity. We consider that aid in design or configuration can be formalized, either completely or partially, in the form of a constraint satisfaction problem (CSP). In this context, we focus more specifically on interactive decision-support, by introducing the principles of filtering or constraint propagation. The diversity of knowledge formalized as a CSP and the interaction with the user allow us to assemble and adapt filtering algorithms in a generic constraint propagation engine, integrated in our CoFiADe software solution.In addition, this formalism based on CSP constraints is complemented by : - ontologies to structure knowledge and facilitate its reuse throughout the development cycle, - analogy-based approaches taking advantage of contextual knowledge encapsulated in the case under study, so as to make recommendations to the user on the choice of values, - evolutionary approaches to optimize the search for multi-criteria solutions.Les travaux de recherche présentés dans ce mémoire trouvent leurs fondements dans les constats suivants :-1- la durée de vie des produits et systèmes tend à se réduire,-2- les conceptions et les actualisations des produits mis sur le marché sont de plus en plus fréquentes alors que les cycles de conception sont toujours plus brefs,-3- les technologies employées en constante évolution nécessitent une acquisition de connaissance permanente,-4- la diversité des produits offerte sur les marchés ne cesse de croître allant des produits personnali- sables ou configurés jusqu’aux produits sur-mesure et conçus à la commande.Ces tendances et la masse d’informations et de connaissances à traiter en découlant exigent des concepteurs toujours plus d’attention et un travail cognitif toujours plus intense. Il en résulte une augmentation des risques, que le produit réponde imparfaitement aux besoins du demandeur, qu’il soit difficilement réalisable et fabricable, ou encore qu’il le soit à un coût prohibitif. L’objectif de nos travaux est donc de limiter ces risques et erreurs en proposant des outils logiciels et des environnements méthodologiques destinés à capitaliser et exploiter des connaissances générales, contextuelles, académiques, expertes ou métier pour aider la conception.Les travaux effectués sur différentes problématiques industrielles ont conduit à prendre en considération deux natures de connaissances relevant du « domaine produit » et de la « diversité produit » conduisant à des problématiques industrielles différentes : la première nature de connaissance recouvre aussi bien des aspects scientifiques et techniques que des règles métier, elle est nécessaire pour la définition du produit et débouche sur des problématiques d’aide à la conception de produit ; la seconde nature est une connaissance liée à la diversité des produits, qui débouche sur les problématiques d’aide à la personnalisation ou configuration de produit.Nous visons à aider un type de conception plutôt « routinier » où de la connaissance de différentes natures et de divers types existe du fait de la récurrence de l’activité. Nous considérons de plus dans nos travaux que l’aide à la conception ou configuration peut se formaliser, complètement ou partiellement, comme un problème de satisfaction de contraintes (CSP). Dans ce cadre, nous nous intéressons plus spécifiquement à l’aide à la décision interactive exploitant les principes de filtrage ou de propagation de contraintes. Notre objectif se décline alors en l’accompagnement des concepteurs dans la construction des solutions répondant au mieux à leurs problèmes, en retirant progressivement de l’espace des solutions, celles qui ne sont plus cohérentes avec les décisions prises, en estimant celles-ci au fil de leur construction et/ou en les optimisant.en complément, nous associons à ce formalisme à base de contraintes CSP :- des ontologies pour structurer les connaissances et faciliter leur réutilisateion sur l’ensemble du cycle de développement,- des approches par analogie exploitant de la connaissance contextuelle encapsulée dans des cas afin de proposer à l’utilisateur des recommandations quant aux choix de valeurs,- des approches évolutionnaires pour optimiser la recherche des solutions de manière multicritère

Preference-based problem solving for constraint programming

Abstract. Combinatorial problems such as scheduling, resource allocation, and configuration have many attributes that can be subject of user preferences. Traditional optimization approaches compile those preferences into a single utility function and use it as the optimization objective when solving the problem, but neither explain why the resulting solution satisfies the original preferences, nor indicate the trade-offs made during problem solving. We argue that the whole problem solving process becomes more transparent and controllable for the user if it is based on the original preferences. We show how the original preferences can be used to control the problem solving process and how they can be used to explain the choice and the optimality of the detected solution. Based on this explanation, the user can refine the preference model, thus gaining full control over the problem solver.