Proceedings of the Fifth European Workshop on Probabilistic Graphical Models

Peer reviewe

Cooperation of Combinatorial Solvers for Air Traffic Management and Control

In the context of the SESAR project, Air Traffic Control (ATC) and Management (ATM) in Europe is undergoing a paradigm shift to be able to accommodate the current traffic growth forecast: many expert-based systems will be enhanced by optimization software to improve the decisionmaking process and regulation planning. Current state-of-the-art combinatorial optimization techniques that are applied to ATC and ATM include approximation algorithms like metaheuristics (e.g. Genetic Algorithm, Tabu Search, Simulated Annealing, etc.) and complete algorithms like Constraint Programming (CP) and Mixed Integer Programming. However, the large scale of the considered instances and the handling of their inherent uncertainties result in very hard problems, which can hinder or even defeat either of the previously mentioned optimization methods alone. To overcome these difficulties and improve the resolution efficiency of standard algorithms, we propose to study the generic cooperation of any set of combinatorial solvers by sharing solutions, optimization bounds and possibly other information in order to speed up the overall process. In this thesis, we have specified and implemented a distributed system which is able to integrate any combinatorial solver with the suitable interface, adapt existing solvers to take into account and provide information on the state of the search from and to other solvers, and applied this framework to two ATC and ATM problems: the en-route conflict resolution problem and the Gate Allocation Problem (GAP). For the first one, we have presented a new generic framework for the modeling and resolution of en-route conflicts in three dimensions as well as a large set of realistic instances, which have been solved with the cooperation of a Memetic Algorithm and Integer Linear Programming (ILP) solver. For the GAP, we have presented a new CP model, as well as new optimization constraints to maximize the robustness of the schedule, and search strategies together with their parallel cooperation. The solver, implemented with the FaCiLe CP library, outperforms a state-of-the-art ILP solver on real instances

Graph Models for Rational Social Interaction

A fundamental issue in multi-agent systems is to extract a consensus from a group of agents with different perspectives. Even if the bilateral relationships (reflecting the outcomes of disputes, product comparisons, or evaluation of political candidates) are rational, the collective output may be irrational (e.g., intransitivity of group preferences). This motivates AI’s research for devising social outcomes compatible with individual positions. Frequently, such situations are modeled as graphs. While the preponderance of formal theoretical studies of such graph based-models has addressed semantic concerns for defining a desirable output in order to formalize some high-level intuition, results relating to algorithmic and computational complexity are also of great significance from the computational point of view. The first Part of this thesis is devoted to combinatorial aspects of Argumentation Frameworks related to computational issues. These abstract frameworks, introduced by Dung in 1995, are directed graphs with nodes interpreted as arguments and the directed edges as attacks between the arguments. By designing a conflict-resolution formalism to make distinction among acceptable and unacceptable arguments, Dung initiated an important area of research in Artificial Intelligence. I prove that any argumentation framework can be syntactically augmented into a normal form preserving the semantic properties of the original arguments, by using a cubic time rewriting technique. I introduce polyhedral labellings for an argumentation frameworks, which is a polytope with the property that its integral points are exactly the incidence vectors of specific types of Dung’s outcome. Also, a new notion of acceptability of arguments is considered – deliberative acceptability – and I provide it’s time computational complexity analysis. This part extends and improves some of the results from the my Master thesis. In the second Part, I introduce a novel graph-based model for aggregating preferences. By using graph operations to describe properties of the aggregators, axiomatic characterizations of aggregators corresponding to usual majority or approval & disapproval rule are given. Integrating Dung’s semantics into our model provides a novel qualitative approach to classical social choice: argumentative aggregation of individual preferences. Also, a functional framework abstracting many-to-many two-sided markets is considered: the study of the existence of a Stable Choice Matching in a Bipartite Choice System is reduced to the study of the existence of Stable Common Fixed Points of two choice functions. A generalization of the Gale-Shapley algorithm is designed and, in order to prove its correctness, a new characterization of path independence choice functions is given. Finally, in the third Part, we extend Dung’s Argumentation Frameworks to Opposition Frameworks, reducing the gap between Structured and Abstract Argumentation. A guarded attack calculus is developed, giving proper generalizations of Dung’s extensions.Ein grundlegendes Problem von Multiagentensystemen ist, eine Gruppe von Agenten mit unterschiedlichen Perspektiven zum Konsens zu bringen.W¨ahrend die bilaterale Ergebnisse von Rechtsstreitigkeiten, Produktvergleichen sowie die Bewertung von politischen Kandidaten wiederspiegelnden Beziehungen rational sein sollten, k¨onnte der kollektive Ausgang irrational sein z.B. durch die Intransitivit¨at von Pr¨aferenzen der Gruppe. Das motiviert die KI-Forschung zur Entwicklung von sozialen Ergebnissen, welche mit individuellen Einstellungen kompatibel sind. H¨aufig werden solche Situationen als Graphen modelliert. W¨ahrend die meisten formalen theoretischen Studien von Graphmodellen sich mit semantischen Aspekten f¨ur die Definition eines w¨unschenswerten Ausgangs zur Formalisierung auf hohem Intuitionsniveau besch¨aftigen, ist es ebenfalls von großer Bedeutung, die Komplexit¨at von Algorithmen und Berechnungen zu verstehen. Der erste Teil der vorliegenden Arbeit widmet sich den kombinatorischen Aspekten von Argumentation Frameworks im Zusammenhang mit rechnerischen Fragen. Diese von Dung in 1995 eingef ¨uhrten abstrakten Frameworks sind gerichtete Graphen mit als Argumenten zu interpretierenden Knoten, wobei die gerichteten Kanten Angriffe zwischen den Argumenten sind. Somit hat Dung mit seiner Gestaltung eines Konfliktl¨osungsformalismus zur Unterscheidung zwischen akzeptablen und inakzeptablen Argumenten f¨ur einen wichtigen Bereich von Forschung in KI den Grundstein gelegt. Die Verfasserin hat bewiesen, dass jedes Argumentation Framework sich in einer die semantischen Eigenschaften der originalen Argumente bewahrenden normalen Form syntaktisch erweitern l¨asst, indem man eine mit kubischer Laufzeit umwandelnde Technik verwendet. Neu eingef¨urt werden hier Polyhedrische Etiketten f¨ur Argumentation Frameworks. Dabei handelt es sich um einen Polytop, wessen ganze Punkte genau die Inzidenzvektoren von bestimmten Arten von Dungs Ausgabe sind. Weiterhin wird ein neuer Begriff der Akzeptanz von Argumenten gepr¨agt, n¨amlich - deliberative Akzeptanz - und dessen Komplexit¨at analysiert. Dieser Teil erweitert und verfeinert einige ihrer Ergebnisse aus der Masterarbeit. Im zweiten Teil wurde ein neuartiges graphenbasiertes Modell f¨ur die Aggregation von Pr¨aferenzen erarbeitet. Hier werden axiomatische Charakterisierungen von Aggregatoren neu eingef¨uhrt, und zwar durch die Verwendung von Graphoperationen zur Beschreibung der Eigenschaften von Aggregatoren. Sie entsprechen dem ¨ublichen Mehrheitsprinzip bzw. der Genehmigungs- & Ablehnungsregel. Einen neuartigen, qualitativen Ansatz im Vergleich zu der klassischen Sozialwahltheorie bietet die Integration der Semantik von Dung in dem neuen Modell, und zwar argumentative Aggregation individueller Pr¨aferenzen. Desweiteren wird ein funktionales many to many zweiseitige M¨arkte abstrahierendes Framework untersucht, indem statt die Existenz einer Stabilen Wahl Matching in einem Bipartite Wahlsystem zu studieren, wird die Existenz von Stable Common Fixed Points auf zwei Wahlfunktionen erforscht. Im n¨achsten Schritt wird eine neue Verallgemeinerung des Gale-Shapley Algorithmus entworfen und eine neue Charakterisierung der Wegunabh¨angigkeitsfunktion gegeben, die einen Korrektheitsbeweis f¨ur den Algorithmus erm¨oglicht. Im dritten Teil werden schließlich Dungs Argumentation Frameworks auf Opposition Frameworks erweitert und dadurch die in der gegenw¨artigen Forschung bestehende L¨ucke zwischen strukturierter und abstrakter Argumentation verringert. Daf¨ur wird ein bewachter Angriffskalk¨ul entwickelt, welches strikten Verallgemeinerungen von Dungs echten Erweiterungen f¨uhrt

A list of parameterized problems in bioinformatics

In this report we present a list of problems that originated in bionformatics. Our aim is to collect information on such problems that have been analyzed from the point of view of Parameterized Complexity. For every problem we give its definition and biological motivation together with known complexity results.Postprint (published version

Biomedical applications of belief networks

Biomedicine is an area in which computers have long been expected to play a significant role. Although many of the early claims have proved unrealistic, computers are gradually becoming accepted in the biomedical, clinical and research environment. Within these application areas, expert systems appear to have met with the most resistance, especially when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is necessary to provide the information needed to make rational judgements concerning the inferences the system has made. This entails an explanation of what inferences were made, how the inferences were made and how the results of the inference are to be interpreted. Furthermore there must be a consistent approach to the combining of information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses. Until recently ad hoc formalisms were seen as the only tractable approach to reasoning under uncertainty. A review of some of these formalisms suggests that they are less than ideal for the purposes of decision making. Belief networks provide a tractable way of utilising probability theory as an inference formalism by combining the theoretical consistency of probability for inference and decision making, with the ability to use the knowledge of domain experts.nowledge of domain experts. The potential of belief networks in biomedical applications has already been recog¬ nised and there has been substantial research into the use of belief networks for medical diagnosis and methods for handling large, interconnected networks. In this thesis the use of belief networks is extended to include detailed image model matching to show how, in principle, feature measurement can be undertaken in a fully probabilistic way. The belief networks employed are usually cyclic and have strong influences between adjacent nodes, so new techniques for probabilistic updating based on a model of the matching process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used to apply the belief network formalism to two application domains. The first application is model-based matching in fetal ultrasound images. The imaging modality and biological variation in the subject make model matching a highly uncertain process. A dynamic, deformable model, similar to active contour models, is used. A belief network combines constraints derived from local evidence in the image, with global constraints derived from trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of evidence occurring during the classification of objects on a cervical smear slide as part of an automated pre-screening system. A belief network provides both an explicit domain model and a mechanism for the incremental aggregation of evidence, two attributes important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features required of a decision support system with desirable qualitative features that will lead to improved acceptability of expert systems in the biomedical domain

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..