Integrating Grounding in the Search Process for Answer Set Computing

Answer Set Programming (ASP) is a very convenient paradigmto represent knowledge in Artificial Intelligence and to encode Constraint Satisfaction Problems. For that, the natural way to use ASP is to elaborate a first order logic program with default negation encoding the problem to solve. In a preliminary step this program is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (models) of the program, each answer set encoding one solution of the initial problem. Today, we can say that almost all ASP solvers follow this approach of two steps computation. In this work, we begin by putting in evidence that sometimes the preliminary grounding phase is the only bottleneck for the answer set computation. We show that a lot of useless and counterintuitive work is done in some situations. But, our major contribution is to introduce a new approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. Furthermore, we describe the main lines of the first implementation of our new ASP solver ASPeRiX developed following the introduced methodology

Destabilization of a flow focused suspension of magnetotactic bacteria

Active matter is a new class of material, intrinsically out-of equilibrium with intriguing properties. So far, the recent upsurge of studies has mostly focused on the spontaneous behavior of these systems --in the absence of external constraints or driving--. Yet, many real life systems evolve under constraints, being both submitted to flow and various taxis. In the present work, we demonstrate a new experimental system which opens up the way for quantitative investigations, and discriminating examinations, of the challenging theoretical description of such systems. We explore the behavior of magnetotactic bacteria as a particularly rich and versatile class of driven matter, which behavior can be studied under contrasting and contradicting stimuli. In particular we demonstrate that the competing driving of an orienting magnetic field and hydrodynamic flow lead not only to jetting, but also unveils a new pearling instability. This illustrates new structuring capabilities of driven active matter

Possibilistic Uncertainty Handling for Answer Set Programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal

Smoother than smooth: increasing the flow conveyance of an open-channel flow by using drag reduction methods

International audienceThe drag reduction method using polymer additives is a common strategy to minimize friction losses when carrying fluids (water, oil, or slurries) in pipes over long distances. Previous studies showed that the interactions between the polymer and turbulent structures of the flow tend to modify the streamwise velocity profile close to the walls by adding a so-called elastic sublayer between the classical viscous and log layers. The gain in linear head losses can reach up to 80% depending on the roughness of the walls and the concentration of polymers. The application of this technique to sewers and the subsequent gain in discharge capacity motivated this work to quantitatively measure the drag reduction in classical open-channel flows. Three measurement campaigns were performed in a dedicated long flume for several water discharges and several polymer concentrations: backwater curves over smooth and rough channel walls (including velocity and turbulent shear-stress profiles) and flows around emerging obstacles. The addition of polymers, even in limited concentrations, allowed a high friction decrease with the typical Darcy-Weisbach coefficient reduced by factors of 2 and 1.5, respectively, in smooth and rough walls configurations without obstacles, but without strong modifications of the nondimensional velocity profiles. In contrast, when adding emerging obstacles, the flow was unaffected by the inclusion of polymers, in agreement with the prediction of the literature. The drag reduction method by addition of small concentrations of polymers thus appears to be a promising technique to increase flow conveyance in open-channel flows

A First Order Forward Chaining Approach for Answer Set Computing

The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a Constraint Satisfaction Problem is to elaborate a first order logic program with default negation. In a preliminary step this program, with variables, is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this work, our major contribution is to introduce a new approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. Our methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced constants. We have implemented this strategy in our new ASP solver ASPeRiX. The first benefit of our work is to avoid the bottleneck of instantiation phase arising for some problems because of the huge amount of memory needed to ground all rules of a program, even if these rules are not really useful in certain cases. The second benefit is to make the treatment of function symbols easier and without syntactic restriction provided that rules are safe

The First Version of a New ASP Solver : ASPeRiX

We present the first version of our ASP solver ASPeRiX that implements a new approach of answer set computation. The main specifity of our system is to realize a forward chaining of first order rules that are grounded on the fly. So, unlike all others available ASP systems ASPeRiX does not need a pregrounding processing

ASPeRiX: un solveur ASP du premier ordre

Nous présentons ASPeRiX, un solveur ASP dont la spécificité est detravailler directement avec des règles logiques avec variables et donc sans instanciation préalable du programme comme cela est pratiqué par les principaux solveurs actuels. Le système applique des règles en chaînage avant en les instanciant au fur et à mesure des besoins. ASPeRiX évite ainsi certaines difficultés auxquelles se heurtent les solveurs ASP traditionnels

Contextualisation de la sélection automatique de caméras : diffusion d'un match de basketball

International audienceThis study proposes a methodology for automatic camera selection based on the application context modeling. The integration of knowledge about the event makes it possible to identify the sources of interest in the scene. This methodology is applied in the context of the broadcasting of a basketball match. The automatic camera selection is based on the location of the action of interest and the detection of free throws in the video streams.Cette étude propose une méthodologie de sélection automatique de caméras se basant sur la modélisation du contexte d'application. L'intégration de connaissances sur l'évènement permet d'identifier les sources d'intérêts dans la scène. Cette méthodologie est appliquée dans le cadre de la diffusion d'un match de basket. La sélection automatique de la caméra est basée sur la localisation de l'action d'intérêt et la détection des lancers-francs dans les flux vidéos