Learning for Dynamic subsumption

In this paper a new dynamic subsumption technique for Boolean CNF formulae is proposed. It exploits simple and sufficient conditions to detect during conflict analysis, clauses from the original formula that can be reduced by subsumption. During the learnt clause derivation, and at each step of the resolution process, we simply check for backward subsumption between the current resolvent and clauses from the original formula and encoded in the implication graph. Our approach give rise to a strong and dynamic simplification technique that exploits learning to eliminate literals from the original clauses. Experimental results show that the integration of our dynamic subsumption approach within the state-of-the-art SAT solvers Minisat and Rsat achieves interesting improvements particularly on crafted instances

ALPACAS: A Language for Parametric Assessment of Critical Architecture Safety

This paper introduces Alpacas, a domain-specific language and algorithms aimed at architecture modeling and safety assessment for critical systems. It allows to study the effects of random and systematic faults on complex critical systems and their reliability. The underlying semantic framework of the language is Stochastic Guarded Transition Systems, for which Alpacas provides a feature-rich declarative modeling language and algorithms for symbolic analysis and Monte-Carlo simulation, allowing to compute safety indicators such as minimal cutsets and reliability. Built as a domain-specific language deeply embedded in Scala 3, Alpacas offers generic modeling capabilities and type-safety unparalleled in other existing safety assessment frameworks. This improved expressive power allows to address complex system modeling tasks, such as formalizing the architectural design space of a critical function, and exploring it to identify the most reliable variant. The features and algorithms of Alpacas are illustrated on a case study of a thrust allocation and power dispatch system for an electric vertical takeoff and landing aircraft

ALPACAS: A Language for Parametric Assessment of Critical Architecture Safety (Artifact)

This artifact contains a virtual machine allowing to use ALPACAS, a domain-specific language and algorithms aimed at architecture modeling and safety assessment for critical systems. ALPACAS allows to study the effects of random and systematic faults on complex critical systems and their reliability. The underlying semantic framework of the language is Stochastic Guarded Transition Systems, for which ALPACAS provides a feature-rich declarative modeling language and algorithms for symbolic analysis and Monte-Carlo simulation, allowing to compute safety indicators such as minimal cutsets and reliability. Built as a domain-specific language deeply embedded in Scala 3, ALPACAS offers generic modeling capabilities and type-safety unparalleled in other existing safety assessment frameworks. This improved expressive power allows to address complex system modeling tasks, such as formalizing the architectural design space of a critical function, and exploring it to identify the most reliable variant. The features and algorithms of ALPACAS are illustrated on a case study of a thrust allocation and power dispatch system for an electric vertical takeoff and landing aircraft

Interactive Optimization With Weighted Hypervolume Based EMO Algorithms: Preliminary Experiments

The objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Nowadays, for example within the scope of sustainable development, many objectives are taken into account: besides classical objectives such as cost and profit, some new objectives like energy consumption, noise levels or risks have to be considered. With more and more objectives, the number of incomparable alternatives typically increases and the complexity of these problems does not make it easy for a decision maker to formalize preferences towards a specific solution or not even towards a specific but small enough portion of the search space. Moreover, also the algorithms themselves have difficulties to find a good approximation of the entire Pareto front if the number of incomparable solutions increases and the Pareto dominance relation does not indicate a good search direction anymore. In this case, combining the decision making with the search algorithm to an interactive optimization algorithm is considered as a valuable approach. While better and better solutions are found by the optimization algorithm, the DM can specify the preferences more and more precisely while learning about the problem and the objectives' inherent tradeoffs. Such an interactive approach should profit from evaluating solutions only within the interesting regions of the search space in terms of a faster convergence towards the DM's preferred solutions. In the field of EMO, interactive optimization has only been considered recently and in comparison to the vast amount of general EMO algorithms, significantly less interactive EMO algorithms exist. Although, for example, optimization algorithms based on the weighted hypervolume indicator allow to incorporate various preference types into the search, no effort has been made to use this concept within an interactive algorithm. In this report, we propose and discuss how to combine interactive decision making and weighted hypervolume based search algorithms. We focus on a basic model where the DM is asked to pick the most desirable solution among a set. Several examples on standard test problems show the working principles and the usefulness of the interactive approach, in particular with respect to the proximity of the algorithm's population to the DM's most preferred solution.Les fonctions objectif en optimisation multi-objectif sont souvent non-linéaires, bruitées ou non-disponibles et l'optimisation multi-objectif évolutionnaire est applicable dans ce cas. De nos jours, par exemple dans le développement durable, plusieurs objectifs peuvent être pris en compte : en plus des objectifs "classiques" comme le coût et le profit, de "nouveaux" objectifs comme consommation d'énergie, niveaux de bruits ou de risque sont considérés. Avec de plus en plus d'objectifs à prendre en compte, le nombre d'alternatives incomparables croit exponentiellement et la complexité de ces problèmes ne permet pas aux décideurs de formaliser ses préférences afin de calculer une solution spécifique ou même restreindre la recherche à un petit ensemble d'alternatives. De plus, les algorithmes ont des difficultés à trouver une bonne approximation de la région Pareto si le nombre d'alternatives incomparables est grand et la relation de dominance de Pareto ne permet plus une bonne direction de la recherche. Dans ce cas, combiner les algorithmes de recherche et la prise de décision en un algorithme d'optimisation interactif est considérée comme une approche alternative. Pendant que de meilleures solutions sont trouvées par l'algorithme d'optimisation, le décideur peut spécifier ses préférences de manière de plus en plus spécifique en apprenant le problème et le compromis entre les objectifs. Une telle approche interactive devrait bénéficier de l'évaluation des solutions seulement dans des régions intéressantes de l'espace de recherche en terme d'une convergence plus rapide vers les solutions préférées pour le décideur. Dans le domaine de l'optimisation multi-objectif évolutionnaire, l'optimisation interactive a été seulement considérée récemment et en comparaison au grand nombre algorithmes d'optimisation multi-objectif évolutionnaire, peu d'algorithmes d'optimisation multi-objectif évolutionnaire interactifs existent. Bien que, par exemple, des algorithmes d'optimisation basés sur l'indicateur d'hyper-volume pondéré permettent d'inclure plusieurs types de préférences dans la recherche, aucun effort n'a été fourni pour utiliser ce concept dans les algorithmes interactifs. Dans ce rapport, nous proposons et discutons comment combiner la prise de décision interactive et les algorithmes de recherche basés sur l'hyper-volume pondéré. Nous considérons le modèle basique où le décideur est appelé à choisir les solutions qu'il préfère dans un ensemble de solutions. Plusieurs exemples de problèmes de tests standards montrent les principes et l'intérêt de l'approche interactive, en particulier par rapport à la proximité de la population de l'algorithme aux solutions préférées du décideur

Efficient Combination of Decision Procedure for MUS Computation

International audienceIn recent years, the problem of extracting a MUS (Minimal Unsatisfiable Subformula) from an unsatisfiable CNF has received much attention. Indeed, when a Boolean formula is proved unsatisfiable, it does not necessarily mean that all its clauses take part to the inconsistency; a small subset of them can be conflicting and make the whole set without any solution. Localizing a MUS can thus be extremely valuable, since it enables to circumscribe a minimal set of constraints that represents a cause for the infeasibility of the CNF. In this paper, we introduce a novel, original framework for computing a MUS. Whereas most of the existing approaches are based on complete algorithms, we propose an approach that makes use of both local and complete searches. Our combination is empirically evaluated against the current best techniques on a large set of benchmarks

Subsumption dirigée par l'analyse de conflits

International audienceNon disponibl

Réordonnancement dynamique basé sur l'apprentissage

International audienceNon disponibl

Passenger-Centric Urban Air Mobility: Fairness Trade-Offs and Operational Efficiency

Urban Air Mobility (UAM) has the potential to revolutionize transportation. It will exploit the third dimension to help smooth ground traffic in densely populated areas. To be successful, it will require an integrated approach able to balance efficiency and safety while harnessing common resources and information. In this work we focus on future urban air-taxi services, and present the first methods and algorithms to efficiently operate air-taxi at scale. Our approach is twofold. First, we use a passenger-centric perspective which introduces traveling classes, and information sharing between transport modes to differentiate quality of services. This helps smooth multimodal journeys and increase passenger satisfaction. Second, we provide a flight routing and recharging solution which minimizes direct operational costs while preserving long term battery life through reduced energy-intense recharging. Our methods, which surpass the performance of a general state-of-the-art commercial solver, are also used to gain meaningful insights on the design space of the air-taxi problem, including solutions to hidden fairness issues.Comment: Submitted to Transportation Research Part C: Emerging Technologie