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Distributed Gibbs: A MemoryBounded SamplingBased DCOP Algorithm
"... Researchers have used distributed constraint optimization problems (DCOPs) to model various multiagent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sam ..."
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Cited by 6 (6 self)
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Researchers have used distributed constraint optimization problems (DCOPs) to model various multiagent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) samplingbased algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this paper, we introduce a new samplingbased DCOP algorithm called Distributed Gibbs, whose memory requirements per agent is linear in the number of agents in the problem. Additionally, we show empirically that our algorithm is able to find solutions that are better than DUCT; and computationally, our algorithm runs faster than DUCT as well as solve some large problems that DUCT failed to solve due to memory limitations.
Improving DPOP with branch consistency for solving distributed constraint optimization problems
 In CP
, 2014
"... Abstract. The DCOP model has gained momentum in recent years thanks to its ability to capture problems that are naturally distributed and cannot be realistically addressed in a centralized manner. Dynamic programming based techniques have been recognized to be among the most effective techniques f ..."
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Cited by 4 (4 self)
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Abstract. The DCOP model has gained momentum in recent years thanks to its ability to capture problems that are naturally distributed and cannot be realistically addressed in a centralized manner. Dynamic programming based techniques have been recognized to be among the most effective techniques for building complete DCOP solvers (e.g., DPOP). Unfortunately, they also suffer from a widely recognized drawback: their messages are exponential in size. Another limitation is that most current DCOP algorithms do not actively exploit hard constraints, which are common in many real problems. This paper addresses these two limitations by introducing an algorithm, called BrCDPOP, that exploits arc consistency and a form of consistency that applies to paths in pseudotrees to reduce the size of the messages. Experimental results shows that BrCDPOP uses messages that are up to one order of magnitude smaller than DPOP, and that it can scale up well, being able to solve problems that its counterpart can not. 1
Distributed Constraint Optimization Problems (DCOPs)
"... Researchers have recently introduced a promising new class of Distributed Constraint Optimization Problem (DCOP) algorithms that is based on sampling. This paradigm is very amenable to parallelization since sampling algorithms require a lot of samples to ensure convergence, and the sampling proce ..."
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Researchers have recently introduced a promising new class of Distributed Constraint Optimization Problem (DCOP) algorithms that is based on sampling. This paradigm is very amenable to parallelization since sampling algorithms require a lot of samples to ensure convergence, and the sampling process can be designed to be executed in parallel. This paper presents GPUbased DGibbs (GDGibbs), which extends the Distributed Gibbs (DGibbs) sampling algorithm and harnesses the power of parallel computation of GPUs to solve DCOPs. Experimental results show that GDGibbs is faster than several other benchmark algorithms on a distributed meeting scheduling problem.
Under consideration for publication in Theory and Practice of Logic Programming 1 Logic and Constraint Logic Programming for Distributed Constraint Optimization
, 2003
"... The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e.g., multiagent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized mann ..."
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The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e.g., multiagent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized manner. The state of the art in solving DCOPs relies on the use of adhoc infrastructures and adhoc constraint solving procedures. This paper investigates an infrastructure for solving DCOPs that is completely built on logic programming technologies. In particular, the paper explores the use of a general constraint solver (a constraint logic programming system in this context) to handle the agentlevel constraint solving. The preliminary experiments show that logic programming provides benefits over a stateoftheart DCOP system, in terms of performance and scalability, opening the doors to the use of more advanced technology (e.g., search strategies and complex constraints) for solving DCOPs.
Solving Distributed Constraint Optimization Problems Using Logic Programming
"... This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i) It shows how one can formulate DCOPs as logic programs; (ii) It introduces ASPDPOP, the first DCOP algorithm that is based on logic ..."
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This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i) It shows how one can formulate DCOPs as logic programs; (ii) It introduces ASPDPOP, the first DCOP algorithm that is based on logic programming; (iii) It experimentally shows that ASPDPOP can be up to two orders of magnitude faster than DPOP (its imperativeprogramming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv) It demonstrates the applicability of ASP in the wide array of multiagent problems currently modeled as DCOPs.