Conception bayésienne de mécanismes et quantification de l’équité appliquées à la construction d'horaires personnalisés

RÉSUMÉ : Le problème de la construction des quarts est un problème classique de recherche opérationnelle. Le progrès dans leurs modélisations mathématiques et l'amélioration des puissances de calculs informatiques ont permis d'intégrer des facteurs de plus en plus complexes. L'objectif de cette thèse est d'inclure les préférences des employés à la construction d'horaires. Toutefois, au-delà des difficultés algorithmiques, cet objectif pose de nombreuses problématiques, à la fois nouvelles, générales et fondamentales. Les employés auront-ils intérêt à révéler leurs préférences de manière honnête ? Peut-on s'assurer de ne pas désavantager les employés honnêtes ? Peut-on garantir l'équité entre les employés ? Comment modéliser mathématiquement cette équité de manière convaincante ? Comment utiliser le concept d'équité pour optimiser la construction de quarts avec préférences des employés ? Nombre de ces questions élémentaires sont en lien avec la théorie des jeux. Mais les théories existantes sont insuffisantes pour adresser la complexité du contexte de la construction de quarts personnalisés. Ceci nous amènera à introduire une nouvelle perspective sur la théorie des jeux, puissante et adaptée aux calculs informatiques, qui se fonde sur un nouvel objet mathématique appelé fonction de retour. Au lieu de se focaliser sur les stratégies, la fonction de retour propose de se concentrer sur les conséquences des actions des individus sur l'issue qui leur est consacrée. Alors que ces fonctions de retour se prêtent mieux aux calculs, de façon cruciale, elles possèdent pourtant toute l'information nécessaire à l'étude des jeux. C'est pourquoi nous utiliserons alors ce nouvel objet mathématique pour une optimisation heuristique de mécanismes favorisant l'honnêteté dans un contexte simplifié de partage équitable de gâteau. Par ailleurs, nous introduirons de nouvelles définitions de l'équité fondées sur des concepts de distributions, d'échanges et de réseaux sociaux. Ces concepts viennent naturellement compléter la théorie des fonctions d'utilité de von Neumann and Morgenstern (1944), qui ne permet pas de déterminer de manière unique et sensée une quantification de la satisfaction des employés. Enfin, en dernier lieu, nous appliquerons les concepts de cette thèse à des instances de construction de quarts avec préférences des employés. Ceci fera intervenir la théorie du multicritère, ainsi qu'un lourd programme d'optimisation en nombres entiers. Toutefois, faute de temps et à cause de la complexité des calculs numériques, nous ne chercherons pas à favoriser l'honnêteté dans cette application.----------ABSTRACT : Shift scheduling is a classical problem of operations research. As mathematical modelings progress and computational capabilities improve, more complex issues are addressed. The main goal of this PhD thesis is to include employees' preferences. Yet, beside algorithmic considerations, our goal raises various new, general and fundamental questions. Will employees have incentives to reveal their preferences truthfully? Can we make sure that truthful employees are not disadvantaged? Can we guarantee the fairness of the shift allocation? How can fairness be formalized mathematically? How can we use a formal definition of fairness to optimize shift scheduling with employees' preferences? Many of these questions are related to game theory. However, game theory yields unsufficient tools to address the complexity of a shift scheduling scheme. This will lead us to introduce a new perspective on game theory, which we will argue to be both insightful and more computable. This perspective is based on a new object called the return function. Instead of concentrating on strategies, this return function drives focus on the way individuals' actions affect their outcomes. While return functions appear to be more tractable, importantly, they still contain all relevant information for the study of games. Hence, we will use this object to design a heuristical method to optimize over incentive-compatible mechanisms in a simplified fair cake-cutting problem. Furthermore, we will introduce new measures of fairness that are based on ideas of distributions, trades and social networks. These measures will rely on a natural completion of the utility theory by von Neumann and Morgenstern (1944), which does not yield a unique and sensible way of quantifying one's satisfaction. Finally, we apply concepts to difficult instances of shift scheduling with employees' preferences. This application will require a bit of multicriteria theory, as well as a large-scale integer optimization program. Unfortunately though, because of a lack of time and the hardness of algorithmic computations, we shall not aim at favoring fairness in this application

Truthful and Fair Resource Allocation

How should we divide a good or set of goods among a set of agents? There are various constraints that we can consider. We consider two particular constraints. The first is fairness - how can we find fair allocations? The second is truthfulness - what if we do not know agents valuations for the goods being allocated? What if these valuations need to be elicited, and agents will misreport their valuations if it is beneficial? Can we design procedures that elicit agents' true valuations while preserving the quality of the allocation? We consider truthful and fair resource allocation procedures through a computational lens. We first study fair division of a heterogeneous, divisible good, colloquially known as the cake cutting problem. We depart from the existing literature and assume that agents have restricted valuations that can be succinctly communicated. We consider the problems of welfare-maximization, expressiveness, and truthfulness in cake cutting under this model. In the second part of this dissertation we consider truthfulness in settings where payments can be used to incentivize agents to truthfully reveal their private information. A mechanism asks agents to report their private preference information and computes an allocation and payments based on these reports. The mechanism design problem is to find incentive compatible mechanisms which incentivize agents to truthfully reveal their private information and simultaneously compute allocations with desirable properties. The traditional approach to mechanism design specifies mechanisms by hand and proves that certain desirable properties are satisfied. This limits the design space to mechanisms that can be written down and analyzed. We take a computational approach, giving computational procedures that produce mechanisms with desirable properties. Our first contribution designs a procedure that modifies heuristic branch and bound search and makes it usable as the allocation algorithm in an incentive compatible mechanism. Our second contribution draws a novel connection between incentive compatible mechanisms and machine learning. We use this connection to learn payment rules to pair with provided allocation rules. Our payment rules are not exactly incentive compatibility, but they minimize a measure of how much agents can gain by misreporting.Engineering and Applied Science

Essays on Online Learning and Resource Allocation

This thesis studies four independent resource allocation problems with different assumptions on information available to the central planner, and strategic considerations of the agents present in the system. We start off with an online, non-strategic agents setting in Chapter 1, where we study the dynamic pricing and learning problem under the Bass demand model. The main objective in the field of dynamic pricing and learning is to study how a seller can maximize revenue by adjusting price over time based on sequentially realized demand. Unlike most existing literature on dynamic pricing and learning, where the price only affects the demand in the current period, under the Bass model, price also influences the future evolution of demand. Finding arevenue-maximizing dynamic pricing policy in this model is non-trivial even in the full information case, where model parameters are known. We consider the more challenging incomplete information problem where dynamic pricing is applied in conjunction with learning the unknown model parameters, with the objective of optimizing the cumulative revenues over a given selling horizon of length . Our main contribution is an algorithm that satisfies a high probability regret guarantee of order ²/³; where the market size is known a priori. Moreover, we show that no algorithm can incur smaller order of loss by deriving a matching lower bound. We then switch our attention to a single round, strategic agents setting in Chapter 2, where we study a multi-resource allocation problem with heterogeneous demands and Leontief utilities. Leontief utility function captures the idea that for certain resource allocation settings, the utility of marginal increase in one resource depends on the availabilities of other resources. We generalize the existing literature on this model formulation to incorporate more constraints faced in real applications, which in turn requires new algorithm design and analysis techniques. The main contribution of this chapter is an allocation algorithm that satisfies Pareto optimality, envy-freenss, strategy-proofness, and a notion of sharing incentive. In Chapter 3, we study a single round, non-strategic agent setting, where the central planner tries to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Additionally, we want to ensure fairness by controlling the amount of envy that agents have with the final allocations. We make the observation that this resource allocation setting can be formulated as an Optimal Transport problem, and that the solution structure displays a surprisingly simple structure. Using this insight, we are able to design an allocation algorithm that achieves the optimal trade-off between efficiency and envy. Finally, in Chapter 4 we study an online, strategic agent setting, where similar to the previous chapter, the central planner needs to allocate a pool of items to a set of agents who each has to receive a prespecified fraction of all items. Unlike in the previous chapter, the central planner has no a priori information on the distribution of items. Instead, the central planner needs to implicitly learn these distributions from the observed values in order to pick a good allocation policy. Additionally, an added challenge here is that the agents are strategic with incentives to misreport their valuations in order to receive better allocations. This sets our work apart both from the online auction mechanism design settings which typically assume known valuation distributions and/or involve payments, and from the online learning settings that do not consider strategic agents. To that end, our main contribution is an online learning based allocation mechanism that is approximately Bayesian incentive compatible, and when all agents are truthful, guarantees a sublinear regret for individual agents' utility compared to that under the optimal offline allocation policy

Resource-aware plan recognition in instrumented environments

This thesis addresses the problem of plan recognition in instrumented environments, which is to infer an agent';s plans by observing its behavior. In instrumented environments such observations are made by physical sensors. This introduces specific challenges, of which the following two are considered in this thesis: - Physical sensors often observe state information instead of actions. As classical plan recognition approaches usually can only deal with action observations, this requires a cumbersome and error-prone inference of executed actions from observed states. - Due to limited physical resources of the environment it is often not possible to run all sensors at the same time, thus sensor selection techniques have to be applied. Current plan recognition approaches are not able to support the environment in selecting relevant subsets of sensors. This thesis proposes a two-stage approach to solve the problems described above. Firstly, a DBN-based plan recognition approach is presented which allows for the explicit representation and consideration of state knowledge. Secondly, a POMDP-based utility model for observation sources is presented which can be used with generic utility-based sensor selection algorithms. Further contributions include the presentation of a software toolkit that realizes plan recognition and sensor selection in instrumented environments, and an empirical evaluation of the validity and performance of the proposed models.Diese Arbeit behandelt das Problem der Planerkennung in instrumentierten Umgebungen. Ziel ist dabei das Erschließen der Pläne des Nutzers anhand der Beobachtung seiner Handlungen. In instrumentierten Umgebungen erfolgt diese Beobachtung über physische Sensoren. Dies wirft spezifische Probleme auf, von denen zwei in dieser Arbeit näher betrachtet werden: - Physische Sensoren beobachten in der Regel Zustände anstelle direkter Nutzeraktionen. Klassische Planerkennungsverfahren basieren jedoch auf der Beobachtung von Aktionen, was bisher eine aufwendige und fehlerträchtige Ableitung von Aktionen aus Zustandsbeobachtungen notwendig macht. - Aufgrund beschränkter Resourcen der Umgebung ist es oft nicht möglich alle Sensoren gleichzeitig zu aktivieren. Aktuelle Planerkennungsverfahren bieten keine Möglichkeit, die Umgebung bei der Auswahl einer relevanten Teilmenge von Sensoren zu unterstützen. Diese Arbeit beschreibt einen zweistufigen Ansatz zur Lösung der genannten Probleme. Zunächst wird ein DBN-basiertes Planerkennungsverfahren vorgestellt, das Zustandswissen explizit repräsentiert und in Schlussfolgerungen berücksichtigt. Dieses Verfahren bildet die Basis für ein POMDP-basiertes Nutzenmodell für Beobachtungsquellen, das für den Zweck der Sensorauswahl genutzt werden kann. Des Weiteren wird ein Toolkit zur Realisierung von Planerkennungs- und Sensorauswahlfunktionen vorgestellt sowie die Gültigkeit und Performanz der vorgestellten Modelle in einer empirischen Studie evaluiert

Value Creation through Co-Opetition in Service Networks

Well-defined interfaces and standardization allow for the composition of single Web services into value-added complex services. Such complex Web Services are increasingly traded via agile marketplaces, facilitating flexible recombination of service modules to meet heterogeneous customer demands. In order to coordinate participants, this work introduces a mechanism design approach - the co-opetition mechanism - that is tailored to requirements imposed by a networked and co-opetitive environment

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors