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96
Decentralised Coordination of Mobile Sensors Using the MaxSum Algorithm
, 2009
"... In this paper, we introduce an online, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. Th ..."
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Cited by 39 (10 self)
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In this paper, we introduce an online, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the maxsum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speedup of up to 92 % for 5 sensors. We empirically evaluate our algorithm against several online adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50 % compared to a greedy strategy.
Bounded approximate decentralised coordination using the maxsum algorithm
 IN DISTRIBUTED CONSTRAINT REASONING WORKSHOP
, 2009
"... In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution qualit ..."
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Cited by 30 (9 self)
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In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution quality. It uses the maxsum algorithm to optimally solve the resulting tree structured constraint network, providing a bounded approximation specific to the particular problem instance. We formally prove that our algorithm provides a bounded approximation of the original problem and we present an empirical evaluation in a synthetic scenario. This shows that the approximate solutions that our algorithm provides are typically within 95 % of the optimum and the approximation ratio that our algorithm provides is typically 1.23.
A Survey on Sensor Networks from a MultiAgent perspective
"... Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. Mult ..."
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Cited by 26 (0 self)
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Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. MultiAgent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous selfaware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research. 1.
DCOPs meet the real world: Exploring unknown reward matrices with applications to mobile sensor networks
, 2009
"... Abstract Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms ..."
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Cited by 26 (6 self)
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Abstract Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decisiontheoretic exploration approaches with DCOPmandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.
Decentralised Coordination in RoboCup Rescue
, 2009
"... Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of e ..."
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Cited by 14 (4 self)
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Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the fires which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely
Optimal decentralised dispatch of embedded generation in the smart grid
 Proc. AAMAS 2012
, 2012
"... Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demandwithgenerationfromintermittentrenewableresources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this e ..."
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Cited by 13 (2 self)
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Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demandwithgenerationfromintermittentrenewableresources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agentbased coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the maxsum algorithm. We go on to show that maxsum applied naïvely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms maxsum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM’s ILOG CPLEX 12.2, and maxsum, for large networks.
A Distributed Anytime Algorithm for Dynamic Task Allocation in Multiagent Systems
 In Proc. of AAAI
, 2011
"... We introduce a novel distributed algorithm for multiagent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fastmaxsum), and give it significant new capabilities: namely, an online pruning procedure that simplifies ..."
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Cited by 11 (3 self)
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We introduce a novel distributed algorithm for multiagent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fastmaxsum), and give it significant new capabilities: namely, an online pruning procedure that simplifies the problem, and a branchandbound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23 % more utility, than stateoftheart approximation algorithms. In addition, our algorithm sends up to 30 % fewer messages than current approaches when the set of agents or tasks changes.
Decentralized Bayesian reinforcement learning for online agent collaboration
 In AAMAS
, 2012
"... Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent years. This is natural, as on one hand, multiagent systems usually possess a structure that determines the allowable interactions among the agents; and on the other ha ..."
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Cited by 9 (2 self)
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Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent years. This is natural, as on one hand, multiagent systems usually possess a structure that determines the allowable interactions among the agents; and on the other hand, the single most pressing need in a cooperative multiagent system is to coordinate the local policies of autonomous agents with restricted capabilities to serve a systemwide goal. The presence of uncertainty makes this even more challenging, as the agents face the additional need to learn the unknown environment parameters while forming (and following) local policies in an online fashion. In this paper, we provide the first Bayesian reinforcement learning (BRL) approach for distributed coordination and learning in a cooperative multiagent system by devising two solutions to this type of problem. More specifically, we show how the Value of Perfect Information (VPI) can be used to perform efficient decentralised exploration in both modelbased and modelfree BRL, and in the latter case, provide a closed form solution for VPI, correcting a decade old result by Dearden, Friedman and Russell. To evaluate these solutions, we present experimental results comparing their relative merits, and demonstrate empirically that both solutions outperform an existing multiagent learning method, representative of the stateoftheart.
Bounded decentralised coordination over multiple objectives
 In AAMAS
, 2011
"... We propose the bounded multiobjective maxsum algorithm (BMOMS), the first decentralised coordination algorithm for multiobjective optimisation problems. BMOMS extends the maxsum messagepassing algorithm for decentralised coordination to compute bounded approximate solutions to multiobjective ..."
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Cited by 9 (0 self)
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We propose the bounded multiobjective maxsum algorithm (BMOMS), the first decentralised coordination algorithm for multiobjective optimisation problems. BMOMS extends the maxsum messagepassing algorithm for decentralised coordination to compute bounded approximate solutions to multiobjective decentralised constraint optimisation problems (MODCOPs). Specifically, we prove the optimality of BMOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multiobjective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for lessconstrained graphs. Moreover, the runtime required by BMOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with 100 agents. Thus, BMOMS brings the problem of multiobjective optimisation well within the boundaries of the limited capabilities of embedded agents.