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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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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.
Deception in Networks of Mobile Sensing Agents
"... Recent studies have investigated how a team of mobile sensors can cope with real world constraints, such as uncertainty in the reward functions, dynamically appearing and disappearing targets, technology failures end changes in the environment conditions. In this study we consider an additional elem ..."
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Cited by 5 (0 self)
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Recent studies have investigated how a team of mobile sensors can cope with real world constraints, such as uncertainty in the reward functions, dynamically appearing and disappearing targets, technology failures end changes in the environment conditions. In this study we consider an additional element, deception by an adversary, which is relevant in many (military) applications. The adversary is expected to use deception to prevent the sensor team from performing its tasks. We employ a game theoretic model to analyze the expected strategy of the adversary and find the best response. More specifically we consider that the adversary deceptively changes the importance that agents give to targets in the area. The opponent is expected to use camouflage in order to create confusion among the sensors regarding the importance of targets, and reduce the team’s efficiency in target coverage. We represent a Mobile Sensor Team problem using the Distributed Constraint Optimization Problem (DCOP) framework. We propose an optimal method for the selection of a position of a single agent facing a deceptive adversary. This method serves as a heuristic for agents to select their position in a full scale problem with multiple agents in a large area. Our empirical study demonstrates the success of our model as compared with existing models in the presence of deceptions.
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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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 for Mobile Sensor Teams (Doctoral Consortium)
"... Coordinating a mobile sensing agents (MST) to adequately position themselves with regards to points of interest generally called targets (e.g., disaster survivors, military targets, or pollution spills), is a challenging problem in many multiagent applications. Such applications are inherently dyna ..."
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Coordinating a mobile sensing agents (MST) to adequately position themselves with regards to points of interest generally called targets (e.g., disaster survivors, military targets, or pollution spills), is a challenging problem in many multiagent applications. Such applications are inherently dynamic due to changes in the environment, technology failures, and incomplete knowledge of the agents. Agents must adaptively respond by changing their locations to continually optimize the coverage of targets. Optimally choosing where to position agents to meet the coverage requirements in a static setting is a known NPhard optimization problem. Doing so in a dynamic distributed environment is a challenging task. In this work I continue to develop and study the DCOP MST model DCOP is a general model of distributed multiagent coordination. A DCOP is constituted of agents, variables, and (soft and hard) constraints between sets of variables that reflect the costs of assignments to the variables. Each agent has exclusive control over a subset of the variables and knows information relevant to its variables, such as the values that can be assigned to them (their domains) and the constraints involving them. The goal is to select an assignment of values to the variables that minimizes the aggregated costs of the constraints. In many ways DCOPs are a natural fit for MST applications, which are inherently decentralized. However, DCOPs fall short in two ways. First, constraints in a MST problem may involve all agents which can result in an exponentialsized constraint structure, which is difficult to solve. Second, DCOP is a static model. In contrast, the coverage problem confronting the agents in realistic applications is highly dynamic. There are three types of dynamism in MST applications: changes in the environment external to the agents, including targets arising, moving, and disappearing, or target coverage requirements being modified by an outside authority; changes inherent to the agents, including sensor failures resulting in targets being missed or false information being disseminated; and changes in the agents' knowledge of the environment, such as the presence of tar gets and the quality with which they can be sensed from different locations. In DCOP MST, agents maintain variables for their physical positions, while each target is represented by a constraint that reflects the quality of coverage of that target. In contrast to conventional, static DCOP, DCOP MST not only permits dynamism but exploits it by restricting variable domains to nearby locations; consequently, variable domains and constraints change as the agents move through the environment. DCOP MST confers three major advantages. It directly represents the multiple forms of dynamism inherent in MSTs. It also provides a compact representation that can be solved efficiently with local search algorithms, with information and communication locality based on physical locality as typically occurs in MST applications. Finally, DCOP MST facilitates organization of the team into multiple subteams that can specialize in different roles and coordinate their activity through dynamic events. We demonstrate how a searchanddetection team responsible for finding new targets and a surveillance subteam tasked with coverage of known targets can effectively work together to improve performance while using the DCOP MST framework to coordinate. We propose different algorithms to meet the specific needs of each subteam and several methods for cooperation between subteams. For the searchanddetection team, we develop an algorithm based on DSA that forces intensive exploration for new targets. For the surveillance subteam, we adapt several wellknown incomplete DCOP algorithms, including the Maximum Gain Messages (MGM) algorithm, the Distributed Stochastic Algorithm (DSA) and the Maxsum algorithm which requires us to develop an efficient method for agents to find the value assignment in their local environment, which is optimal in minimizing the maximum unmet coverage requirement over all targets. In order to avoid an exponential constraint network, instead of choosing from among all possible locations, each agent considers only nearby locations. Constraints thus do not need to involve all agents at all times but only the agents who are close enough to possibly cover the target. The disadvantage of dynamic domains based on physical locality is that adaptations of standard local search algorithms tend to become trapped in local optima where targets beyond the immediate range of the agents go uncovered. To address this shortcoming we develop exploration methods to be used with the local search algorithms. In designing the algorithms that the agents run, we must balance 1725
Distributed Scheduling Using Constraint Optimization and Multiagent Path Planning
"... Abstract. The goal of the distributed scheduling problem is to minimize the global cost of assigning n decentralized workers to m tasks at time points. This problem is further complicated in continuous environments because the entire state space cannot be searched. This paper presents a decentralize ..."
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Abstract. The goal of the distributed scheduling problem is to minimize the global cost of assigning n decentralized workers to m tasks at time points. This problem is further complicated in continuous environments because the entire state space cannot be searched. This paper presents a decentralized approach of dividing the distributed scheduling in continuous environments problem into two subproblems: distributed set covering and distributed multiagent path planning. First, we represent the problem of assigning workers (i.e., covers) to tasks (i.e., sets) as a Distributed Constraint Optimization Problem (DisCOP). Then, the DisCOP solver passes its solution to the distributed multiagent path planner who creates a conflictfree path for each worker to its assigned tasks. By first representing the problem as a DisCOP, it restricts the plan space to only a set of feasible plans. We apply this approach to the scenario of distributed scheduling for unmanned aerial vehicle surveillance. This approach is shown to relieve the strain on the distributed multiagent path planner by reducing the plan space more so than other distributed approaches. 1
— Ludwig van Beethoven (1770–1827) Acknowledgements
"... programme doctoral en Informatique et Communications ..."
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PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR
"... acceptée sur proposition du jury: Prof. K. Aberer, président du jury Prof. B. Faltings, directeur de thèse Prof. J.P. Hubaux, rapporteur Dr J.A. Rodriguez Aguilar, rapporteur ..."
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acceptée sur proposition du jury: Prof. K. Aberer, président du jury Prof. B. Faltings, directeur de thèse Prof. J.P. Hubaux, rapporteur Dr J.A. Rodriguez Aguilar, rapporteur
Research Article A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm
, 2014
"... Copyright © 2014 Duan Peibo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents a new distributed constraint o ..."
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Copyright © 2014 Duan Peibo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the stateoftheart incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. 1.
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.