Existing solutions are very limited (chats, forums...). Many sites exist, for Web-users to express their opinion, but can they build structured arguments and confront them? We try to answer these questions by offering new software based on logic. But then, what kind of logic can describe the arguments of a debate? What is the role of n-opposition theory? In this paper we compare the results of three experiments. First we will introduce some important notions, then we will present our experimental protocol and finally we will present detailed discussion system. We will also explain how we use the n-opposition theory to structure debate and reason. To conclude, we will discuss experiments contexts and results.

We describe in this article a multiagent urban traffic simulation, as we believe individual-based modeling is necessary to encompass the complex influence the actions of an individual vehicle can have on the overall flow of vehicles. We first describe how we build a graph description of the network from purely geometric data, ESRI shapefiles. We then explain how we include traffic related data to this graph. We go on after that with the model of the vehicle agents: origin and destination, driving behavior, multiple lanes, crossroads, and interactions with the other vehicles in day-to-day, ?ordinary? traffic. We conclude with the presentation of the resulting simulation of this model on the Rouen agglomeration.

We study the notion of informedness in a client-consultant setting. Using a software simulator, we examine the extent to which it pays off for consultants to provide their clients with advice that is well-informed, or with advice that is merely meant to appear to be well-informed. The latter strategy is beneficial in that it costs less resources to keep up-to-date, but carries the risk of a decreased reputation if the clients discover the low level of informedness of the consultant. Our experimental results indicate that under different circumstances, different strategies yield the optimal results (net profit) for the consultants.

MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed discrete continuous variables. They can be solved using a Branch-and-Bound scheme combining several methods, such as linear programming, interval analysis, and cutting methods. Our goal is to integrate constraint programming techniques in this framework. Firstly, global constraints can be introduced to reformulate MINLP problems thus leading to clean models and more precise computations. Secondly, interval-based approximation techniques for nonlinear constraints can be improved by taking into account the integrality of variables early. These methods have been implemented in an interval solver and we present experimental results from a set of MINLP instances.

The exploration of complex interaction networks has attracted considerable interest in various fields, ranging from fundamental biology and medicine to statistical physics and information technology.  In -omics disciplines, significant progresses have been made in understanding the large-scale properties and the biological relevance of these interactions. Some properties such as scale-free distribution of nodes connectivity or centrality are aspects commonly described in such complex interaction systems. In many of these studies the analysis of network topology is complemented by a semantic analysis that may rely on  different labels associated to the interacting entities.   One of the bottleneck of these semantic analysis is that they  are computationally costly. In this paper we present a framework to explore abstraction of networks useful to speedup the computation  of ground network measures. Such abstraction mechanisms may be used to efficiently provide accurate approximations of ground network measures.

Arc consistency algorithms are widely used to prune the search space of Constraint Satisfaction Problems (CSPs). Since many researchers associate arc consistency with binary normalized CSPs, there is a confusion between the notion of arc consistency and 2-consistency. 2-consistency guarantees that any instantiation of a value to a variable can be consistently extended to any second variable. Thus, 2-consistency can be stronger than arc-consistency in binary CSPs. In this paper, we present a new algorithm, called 2-C3, which achieves 2-consistency in binary and non-normalized CSPs. This algorithm is a reformulation of the well-known AC3 algorithm. The evaluation section shows that 2-C3 is able to prune more search space than AC3 and AC4.

This article presents two new algorithms whose purpose is to maintain the Max-RPC domain filtering consistency during search with a minimal memory footprint and implementation effort. Both are sub-optimal algorithms that make use of support residues, a backtrack-stable and highly efficient data structure which was successfully used to develop the state-of-the-art AC-3rm algorithm. The two proposed algorithms, Max-RPCrm and L-Max-RPCrm are competitive with best, optimal Max-RPC algorithms, while being considerably simpler to implement. L-Max-RPCrm computes an approximation of the Max-RPC consistency, which is guaranteed to be strictly stronger than AC with the same space complexity and better worst-case time complexity than Max-RPCrm. In practice, the difference in filtering power between L-Max-RPCrm and standard Max-RPC is nearly indistinguishable on random problems. Max-RPCrm and L-Max-RPCrm are implemented into the Choco Constraint Solver through a strong consistency global constraint. This work opens new perspectives upon the development of strong consistency algorithms into constraint solvers.

We study novel approaches for solving of hard combinatorial problems by translation to Boolean Satisfiability (SAT). Our focus is on combinatorial problems that can be represented as a permutation of n objects, subject to additional constraints. In the case of the Hamiltonian Cycle Problem (HCP), these constraints are that two adjacent nodes in a permutation should also be neighbors in the original graph for which we search for a Hamiltonian cycle. We use the absolute SAT encoding of permutations, where for each of the n objects and each of its pos- sible positions in a permutation, a predicate is defined to indicate whether the object is placed in that position. For implementation of this predicate, we compare the direct and logarithmic encodings that have been used previously, against 16 hierarchical parameterizable encodings of which we explore 416 instantiations. We propose the use of enumerative adjacency constraints—that enumerate the possible neighbors of a node in a permutation — instead of, or in addition to the exclusivity adjacency constraints — that exclude impossible neighbors, and that have been applied previously. We study 11 heuristics for efficiently choosing the first node in the Hamiltonian cycle, as well as 8 heuristics for static CNF variable ordering. We achieve at least 4 orders of magnitude average speedup on HCP benchmarks from the phase transition region, relative to the previously used encodings for solving of HCPs via SAT, such that the speedup is increasing with the size of the graphs.

Several explanation and interpretation tasks, such as diagnosis, plan recognition and image interpretation, can be formalized as abductive and consistency reasoning. The interpretation task is usually executed for the purpose of performing actions, e.g., in diagnosis, repair actions or therapy. In some cases actions are the only or the main way for discriminating among alternative explanations. Some proposals address the problem based on a task-independent representation of a domain which includes an ontology or taxonomy of hypotheses and actions. In this paper we rely on the same type of representation, and we point out the role of abstractions in an iterative process where, like in model-based diagnosis and troubleshooting, further observations or actions are proposed to achieve the overall goal of discriminating among candidate hypotheses and, more importantly, performing the appropriate actions for the case at hand. Discrimination is performed up to an appropriate level which depends on the cost of actions (e.g. repair actions or therapy) to be taken based on the results of abduction, and on the cost of additional observations, which should be balanced with the benefits, in terms of more suitable actions, of better discrimination. Abstractions have a significant impact on this trade-off, given that the cost of observing the same phenomenon at different levels of abstraction may be quite different, and choosing a generic action, without information on which specific instance is most appropriate, is, in general, suboptimal.

We present algorithms for testing the satisfiability and finding the tightened transitive closure of conjunctions of addition constraints of the form ± x ± y ≤ d and bound constraints of the form ± x ≤ d where x and y are integer variables and d is an integer constrant. The running time of these algorithms is a cubic polynomial in the number of input constraints. We also describe an efficient matrix representation of addition and bound constraints. The matrix representation provides a easy, algebraic implementation of the satisfiability and tightened transitive closure algorithms. We also outline the use of these algorithms for the improved implementation of abstract interpretation methods based on the octagonal abstract domain.