The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Ferdinando Fioretto - One of the best experts on this subject based on the ideXlab platform.
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solving multiagent constraint optimization problems on the constraint composite graph
Pacific Rim International Conference on Multi-Agents, 2018Co-Authors: Ferdinando Fioretto, Sven Koenig, Hong Xu, T Satish K KumarAbstract:We introduce the Constraint Composite Graph (CCG) for Distributed Constraint Optimization Problems (DCOPs), a popular paradigm used for the description and resolution of cooperative multi-agent problems. The CCG is a novel graphical representation of DCOPs on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum—a popular DCOP Incomplete Algorithm—called CCG-Max-Sum, which is applied to CCGs, and demonstrate its efficiency and effectiveness on DCOP benchmarks based on several network topologies.
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PRIMA - Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph
Lecture Notes in Computer Science, 2018Co-Authors: Ferdinando Fioretto, Sven Koenig, Hong Xu, T. K. Satish KumarAbstract:We introduce the Constraint Composite Graph (CCG) for Distributed Constraint Optimization Problems (DCOPs), a popular paradigm used for the description and resolution of cooperative multi-agent problems. The CCG is a novel graphical representation of DCOPs on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum—a popular DCOP Incomplete Algorithm—called CCG-Max-Sum, which is applied to CCGs, and demonstrate its efficiency and effectiveness on DCOP benchmarks based on several network topologies.
Abdesslem Layeb - One of the best experts on this subject based on the ideXlab platform.
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A new greedy randomised adaptive search procedure for multiple sequence alignment
International Journal of Bioinformatics Research and Applications, 2013Co-Authors: Abdesslem Layeb, Marwa Selmane, Maroua Bencheikh ElhoucineAbstract:The Multiple Sequence Alignment MSA is one of the most challenging tasks in bioinformatics. It consists of aligning several sequences to show the fundamental relationship and the common characteristics between a set of protein or nucleic sequences; this problem has been shown to be NP-complete if the number of sequences is >2. In this paper, a new Incomplete Algorithm based on a Greedy Randomised Adaptive Search Procedure GRASP is presented to deal with the MSA problem. The first GRASP's phase is a new greedy Algorithm based on the application of a new random progressive method and a hybrid global/local Algorithm. The second phase is an adaptive refinement method based on consensus alignment. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed approach.
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A new greedy randomised adaptive search procedure for solving the maximum satisfiability problem
International Journal of Operational Research, 2013Co-Authors: Abdesslem LayebAbstract:The maximum satisfiability problem (Max-Sat) is one of the most known variant of satisfiability problems. The objective is to find the best assignment for a set of Boolean variables that gives the maximum of verified clauses in a Boolean formula. Unfortunately, this problem was showed NP-complete if the number of variable per clause is higher than 3. In this paper, a new Incomplete Algorithm based on a greedy randomised adaptive search procedure (GRASP) is presented to deal with Max 3-Sat problem. The first GRASP’s phase is a new greedy Algorithm based on iterative application of a new version of pure literal elimination technique called weighted literal elimination. In the next stage of the GRASP Algorithm, a modified version of the Walksat procedure is applied using, as initial solutions, the solutions found by the greedy procedure of the GRASP Algorithm. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed approach.
Philippe David - One of the best experts on this subject based on the ideXlab platform.
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A constraint-based approach for examination timetabling using local repair techniques
Lecture Notes in Computer Science, 1998Co-Authors: Philippe DavidAbstract:We present in this paper an Algorithm based upon the Constraint Satisfaction Problem model, used at the Ecole des Mines de Nantes to generate examination timetables. A strong constraint is that the computing time must be less than 1 minute. This led us to develop an Incomplete Algorithm, using local repair techniques, instead of an exhaustive search method. The program has been validated on fifty handmade problems, and has succesfully solved the thirteen real problems.
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PATAT - A Constraint-Based Approach for Examination Timetabling Using Local Repair Techniques
Lecture Notes in Computer Science, 1998Co-Authors: Philippe DavidAbstract:We present in this paper an Algorithm based upon the Constraint Satisfaction Problem model, used at the “ecole des Mines de Nantes” to generate examination timetables. A strong constraint is that the computing time must be less than 1 minute. This led us to develop an Incomplete Algorithm, using local repair techniques, instead of an exhaustive search method. The program has been validated on fifty “hand-made” problems, and has succesfully solved the thirteen “real” problems.
T Satish K Kumar - One of the best experts on this subject based on the ideXlab platform.
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solving multiagent constraint optimization problems on the constraint composite graph
Pacific Rim International Conference on Multi-Agents, 2018Co-Authors: Ferdinando Fioretto, Sven Koenig, Hong Xu, T Satish K KumarAbstract:We introduce the Constraint Composite Graph (CCG) for Distributed Constraint Optimization Problems (DCOPs), a popular paradigm used for the description and resolution of cooperative multi-agent problems. The CCG is a novel graphical representation of DCOPs on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum—a popular DCOP Incomplete Algorithm—called CCG-Max-Sum, which is applied to CCGs, and demonstrate its efficiency and effectiveness on DCOP benchmarks based on several network topologies.
T. K. Satish Kumar - One of the best experts on this subject based on the ideXlab platform.
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PRIMA - Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph
Lecture Notes in Computer Science, 2018Co-Authors: Ferdinando Fioretto, Sven Koenig, Hong Xu, T. K. Satish KumarAbstract:We introduce the Constraint Composite Graph (CCG) for Distributed Constraint Optimization Problems (DCOPs), a popular paradigm used for the description and resolution of cooperative multi-agent problems. The CCG is a novel graphical representation of DCOPs on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum—a popular DCOP Incomplete Algorithm—called CCG-Max-Sum, which is applied to CCGs, and demonstrate its efficiency and effectiveness on DCOP benchmarks based on several network topologies.
