Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair

MP-ABT: A Minimal Perturbation Approach for Complex Local Problems

n/

Profound Degree: A Conservative Heuristic to Repair Dynamic CSPs

Part 4: Hybrid - Changing EnvironmentsInternational audienceFor a better treatment of Dynamic Constraint Satisfaction Problems (DCSPs), several techniques have been developed to be used in repair algorithms. We cite, for example, the variables/values ordering heuristics and local search techniques.We distinguish between static heuristics, which calculate their values once at the beginning of the search, and dynamic heuristics that use an expensive intelligence in terms of solving time.In this paper, we propose a new static variable ordering heuristic, Profound Degree (pdeg), based on deg heuristic, which calculates the degree of influence of a given variable, on the whole constraints network, relatively to its position in the network.We evaluate this heuristic on the Extended Partial-order Dynamic Backtracking (EPBD) approach, which is an approach to repair DCSPs solutions, and we compare it to the best-known variables ordering heuristics (VOHs) for repairing. The evaluation of performance is on random binary problems and meeting scheduling problems, with the criteria of computation time, number of constraints checks and Hamming distance between the former and the current solution

JChoc DisSolver - Bridging the Gap Between Simulation and Realistic Use

The development of innovative and intelligent multiagent applications based on Distributed Constraints Reasoning techniques is obviously a fastidious task, especially to tackle new combinatorial problems (e.i. distributed resource management, distributed air traffic management, Distributed Sensor Network (Bejar et al., ´ 2005)). However, there are very few open-source platforms dedicated to solve such problems within realistic uses. Given the difficulty that researchers are facing, simplifying assumptions and simulations uses are commonly used techniques. Nevertheless, these techniques may not be able to capture all the details about the problem to be solved. Hence, transition from the simulation to the actual development context causes a loss of accuracy and robustness of the applications to be implemented. In this paper, we present preliminary results of a new distributed constraints programming platform, namely JChoc DisSolver. Thanks to the extensibility of JADE communication model and the robustness of Choco Solver, JChoc brings a new added value to Distributed Constraints Reasoning. The platform is user-friendly and the development of multiagent applications based on Constraints Programming is no longer a mystery to users. A real distributed problem is used to illustrate how the platform can be appropriated by an unsophisticated user and the experimental results are encouraging for more investigations

Asynchronous Inter-Level Forward-Checking for DisCSPs

International audienceWe propose two new asynchronous algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). The second algorithm, Asynchronous Inter-Level Forward-Checking (AILFC), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-ng and AILFC only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisC-SPs in two kinds of communication environments: Fast communication and slow communication. Our experiments show that AFC-ng improves on AFC and that AILFC outperforms all compared algorithms in communication load

Dynamic Backtracking for Distributed Constraint Optimization

International audienc

Robust coverage optimization approach in Wireless Sensor Networks

International audienceThe deployment of Wireless Sensor Networks to support optimal coverage is obviously a fastidious task, especially in hard to reach or inaccessible area. To address this problem, simplifying assumptions and simulations are commonly used techniques. However, these techniques may not be able to capture all the details about the problem to be solved. Hence, transition from the simulation to the actual deployment causes a loss of accuracy and robustness in coverage problem. In this paper, we address the above issue using two main ideas. First, pre-processing step with terrain analysis in three-dimensional environment. Second, a robust modeling approach based on both average and variance of visibility from each cell. We formulate the coverage optimization problem as a decision problem, where the objective is to maximize the area covered by a fixed number of sensors. Numerical results show that the proposed techniques are more efficient than the standard-based model, especially in hard to reach areas