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188
Solving transition independent decentralized Markov decision processes
 JAIR
, 2004
"... Formal treatment of collaborative multiagent systems has been lagging behind the rapid progress in sequential decision making by individual agents. Recent work in the area of decentralized Markov Decision Processes (MDPs) has contributed to closing this gap, but the computational complexity of thes ..."
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Cited by 107 (13 self)
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Formal treatment of collaborative multiagent systems has been lagging behind the rapid progress in sequential decision making by individual agents. Recent work in the area of decentralized Markov Decision Processes (MDPs) has contributed to closing this gap, but the computational complexity of these models remains a serious obstacle. To overcome this complexity barrier, we identify a specific class of decentralized MDPs in which the agents ’ transitions are independent. The class consists of independent collaborating agents that are tied together through a structured global reward function that depends on all of their histories of states and actions. We present a novel algorithm for solving this class of problems and examine its properties, both as an optimal algorithm and as an anytime algorithm. To the best of our knowledge, this is the first algorithm to optimally solve a nontrivial subclass of decentralized MDPs. It lays the foundation for further work in this area on both exact and approximate algorithms. 1.
A Survey of MultiAgent Organizational Paradigms
 The Knowledge Engineering Review
, 2005
"... Many researchers have demonstrated that the organizational design employed by a system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this a ..."
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Cited by 103 (2 self)
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Many researchers have demonstrated that the organizational design employed by a system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multiagent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, and matrix organizations. We will provide a description of each, discuss their costs and benefits, and provide examples of how they may be instantiated and maintained. 1
Decentralised Coordination of LowPower Embedded Devices Using the MaxSum Algorithm
 In: 7 th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS08
, 2008
"... This paper considers the problem of performing decentralised coordination of lowpower embedded devices (as is required within many environmental sensing and surveillance applications). Specifically, we address the generic problem of maximising social welfare within a group of interacting agents. We ..."
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Cited by 96 (30 self)
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This paper considers the problem of performing decentralised coordination of lowpower embedded devices (as is required within many environmental sensing and surveillance applications). Specifically, we address the generic problem of maximising social welfare within a group of interacting agents. We propose a novel representation of the problem, as a cyclic bipartite factor graph, composed of variable and function nodes (representing the agents’ states and utilities respectively). We show that such representation allows us to use an extension of the maxsum algorithm to generate approximate solutions to this global optimisation problem through local decentralised message passing. We empirically evaluate this approach on a canonical coordination problem (graph colouring), and benchmark it against state of the art approximate and complete algorithms (DSA and DPOP). We show that our approach is robust to lossy communication, that it generates solutions closer to those of DPOP than DSA is able to, and that it does so with a communication cost (in terms of total messages size) that scales very well with the number of agents in the system (compared to the exponential increase of DPOP). Finally, we describe a hardware implementation of our algorithm operating on lowpower Chipcon CC2431 SystemonChip sensor nodes.
Allocating Tasks in Extreme Teams
 AAMAS'05
, 2005
"... Extreme teams, largescale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic for current task allocation algorithms due to the lack of locality in agent interactions. We propose a novel distributed task allocation algorithm for extreme teams, called ..."
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Cited by 70 (20 self)
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Extreme teams, largescale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic for current task allocation algorithms due to the lack of locality in agent interactions. We propose a novel distributed task allocation algorithm for extreme teams, called LADCOP, that incorporates three key ideas. First, LADCOP's task allocation is based on a dynamically computed minimum capability threshold which uses approximate knowledge of overall task load. Second, LADCOP uses tokens to represent tasks and further minimize communication. Third, it creates potential tokens to deal with intertask constraints of simultaneous execution. We show that LADCOP convincingly outperforms competing distributed task allocation algorithms while using orders of magnitude fewer messages, allowing a dramatic scaleup in extreme teams, upto a fully distributed, proxybased team of 200 agents. Varying threshold are seen as a key to outperforming competing distributed algorithms in the domain of simulated disaster rescue.
BnBAdopt: An Asynchronous BranchandBound . . .
, 2008
"... Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOP problems optimally with memorybounded and asynchronous algorithms. We introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asyn ..."
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Cited by 59 (15 self)
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Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOP problems optimally with memorybounded and asynchronous algorithms. We introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memorybounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOP problems and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOP problems.
A distributed framework for solving the multiagent plan coordination problem
 In AAMAS
, 2005
"... We examine whether and how the Multiagent Plan Coordination Problem, the problem of resolving interactions between the plans of multiple agents, can be cast as a Distributed Constraint Optimization Problem (DCOP). We use ADOPT, a stateoftheart DCOP solver that can solve DCOPs in an asynchronous, ..."
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Cited by 56 (3 self)
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We examine whether and how the Multiagent Plan Coordination Problem, the problem of resolving interactions between the plans of multiple agents, can be cast as a Distributed Constraint Optimization Problem (DCOP). We use ADOPT, a stateoftheart DCOP solver that can solve DCOPs in an asynchronous, parallel manner using local communication between individual computational agents. We then demonstrate how we can take advantage of novel flawassignment strategies and plan coordination algorithms to significantly improve the performance of ADOPT on representative coordination problems. We close with a consideration of possible advances in framing our DCOP representation of the Multiagent
Mdpop: Faithful distributed implementation of efficient social choice problems
 In AAMAS’06  Autonomous Agents and Multiagent Systems
, 2006
"... In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem ..."
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Cited by 48 (17 self)
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In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce MDPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the VickreyClarkeGroves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report
Impact of problem centralization in distributed constraint optimization algorithms
 In AAMAS
, 2005
"... Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the d ..."
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Cited by 47 (4 self)
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Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of “number of cycles ” fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define “CycleBased Runtime (CBR) ” to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.
Quality Guarantees on kOptimal Solutions for Distributed Constraint Optimization Problems
, 2007
"... A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents. Because complete algorithms to solve DCOPs are unsuitable for some dynamic or anytime domains, researchers have explored incomplete DCOP algorithms t ..."
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Cited by 44 (10 self)
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A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents. Because complete algorithms to solve DCOPs are unsuitable for some dynamic or anytime domains, researchers have explored incomplete DCOP algorithms that result in locally optimal solutions. One type of categorization of such algorithms, and the solutions they produce, is koptimality; a koptimal solution is one that cannot be improved by any deviation by k or fewer agents. This paper presents the first known guarantees on solution quality for koptimal solutions. The guarantees are independent of the costs and rewards in the DCOP, and once computed can be used for any DCOP of a given constraint graph structure.
Asynchronous forwardbounding for distributed constraints optimization
 In: Proc. 1st Intern. Workshop on Distributed and Speculative Constraint Processing. (2005
, 2006
"... A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one ..."
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Cited by 42 (8 self)
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A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. Forward bounding propagates the bounds on the cost of solutions by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. Experimental evaluation of AFB on random MaxDisCSPs reveals a phase transition as the tightness of the problem increases. This effect is analogous to the phase transition of MaxCSP when local consistency maintenance is applied [3]. AFB outperforms Synchronous Branch & Bound (SBB) as well as the asynchronous stateoftheart ADOPT algorithm, for the harder problem instances. Both asynchronous algorithms outperform SBB by a large factor. 1