Constraint-based reachability

Iterative imperative programs can be considered as infinite-state systems computing over possibly unbounded domains. Studying reachability in these systems is challenging as it requires to deal with an infinite number of states with standard backward or forward exploration strategies. An approach that we call Constraint-based reachability, is proposed to address reachability problems by exploring program states using a constraint model of the whole program. The keypoint of the approach is to interpret imperative constructions such as conditionals, loops, array and memory manipulations with the fundamental notion of constraint over a computational domain. By combining constraint filtering and abstraction techniques, Constraint-based reachability is able to solve reachability problems which are usually outside the scope of backward or forward exploration strategies. This paper proposes an interpretation of classical filtering consistencies used in Constraint Programming as abstract domain computations, and shows how this approach can be used to produce a constraint solver that efficiently generates solutions for reachability problems that are unsolvable by other approaches.Comment: In Proceedings Infinity 2012, arXiv:1302.310

Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation

The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the pedestrians and other vulnerable road users and potentially to external auditors in case of accidents. However, nowadays, most explainable methods still rely on quantitative analysis of the AD scene representations captured by multiple sensors. This paper proposes a novel representation of AD scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative spatiotemporal reasoning of long-term scenes. The construction of this graph exploits the recent Qualitative Constraint Acquisition paradigm. Our experimental results on NuScenes, an open real-world multi-modal dataset, show that the qualitative eXplainable graph of an AD scene composed of 40 frames can be computed in real-time and light in space storage which makes it a potentially interesting tool for improved and more trustworthy perception and control processes in AD

Solve a Constraint Problem without Modeling It

International audienceWe study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisition does not require positive examples to learn a constraint network. It is thus able to solve a constraint problem without modeling it: we simply exit from QuAcq as soon as a complete example is classified as positive by the user. In this paper, we propose ASK&SOLVE, an elicitation-based solver that tries to find the best tradeoff between learning and solving to converge as soon as possible on a solution. We propose several strategies to speed-up ASK&SOLVE. Finally we give an experimental evaluation that shows that our approach improves the state of the art

Vers une Théorie du Test des programmes à contraintes

International audienceNon disponibl

Closed-Pattern : Une contrainte globale pour l’extraction de motifs fréquents fermés

National audienceL’extraction de motifs fréquents fermés est un des défis majeurs en fouille de données. Les travaux entrepris récemment en extraction de motifs ont mis en avant l’intérêt d’utiliser les contraintes pour une fouille déclarative. Ces approches se sont montrées très attractives par leurs flexibilité, mais l’utilisation d’un nombre important de contraintes réifiées et de variables auxiliaires posent un sérieux problème quant au traitement des bases de grandes tailles. Dans ce papier, nous présentons une contrainte globale nommée ClosedPattern, qui capture la sémantique particulière des motifs fermés pour résoudre efficacement ce problème, sans faire appel aux contraintes réifiées. Nous proposons un algorithme de filtrage pour la contrainte ClosedPattern, qui maintient la consistance de domaine DC en un temps et espace polynomial

Parallel Constraint Acquisition

Constraint acquisition systems assist the non-expert user in modelling her problem as a constraint network. QUACQ is a sequential constraint acquisition algorithm that generates queries as (partial) examples to be classified as positive or negative. The drawbacks are that the user may need to answer a great number of such examples, within a significant waiting time between two examples, to learn all the constraints. In this paper, we propose PACQ, a portfolio-based parallel constraint acquisition system. The design of PACQ benefits from having several users sharing the same target problem. Moreover, each user is involved in a particular acquisition session, opened in parallel to improve the overall performance of the whole system.We prove the correctness of PACQ and we give an experimental evaluation that shows that our approach improves on QUACQ

Towards an MDD-based representation of preferences

International audienceIn a purely constraint programming (CP) context, Andersen et al. [Andersen et al., 2007] proposed to use the Multivalued Decision Diagram structure (MDD) to replace the domain store where constraints have an MDD-Based presentation. An MDD is graphically represented by a (rooted) directed acyclic graph of an ordered list of variables, and can be exponentially smaller than the extensional version of feasible outcomes. Each outcome is encoded as a path in the graph, and each edge in the path encodes a variable assignment. Additionally, an MDD comes with a fast and effective GAC algorithm [Cheng and Yap, 2010], that has time complexity linear to the size of the MDD, and achieves full incrementality in constant time.To take advantage of MDDs we consider the case of preference constrained problems. That is, not all possible outcomes are feasible. In this proposal, we attempt to address the problem of outcomes representation using MDDs where, in our context, domain store represents all possible outcomes and constraints are constraints restricting the feasibility of outcome