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42
BnB-Adopt: An Asynchronous Branch-and-Bound . . .
, 2008
"... Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. It is often desirable to solve DCOP problems optimally with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded 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 agent-coordination problems. It is often desirable to solve DCOP problems optimally with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memory-bounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-andbound search. Our experimental results show that BnB-ADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOP problems and faster than NCBB, a memory-bounded synchronous DCOP algorithm, on most of these DCOP problems.
M-dpop: 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 M-DPOP, 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 Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report
Bounded approximate decentralised coordination using the max-sum 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 max-sum 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 ap-proximation 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.
Anytime local search for distributed constraint optimization
- In Twenty-Third AAAI Conference on Artificial Intelligence
, 2008
"... Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between th ..."
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Cited by 24 (6 self)
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Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between the global evaluation of a system’s state and the private evaluation of states by agents, agents are unaware of the global best state which is explored by the algorithm. Previous attempts to use local search algorithms for solving DisCOPs reported the state held by the system at the termination of the algorithm, which was not necessarily the best state explored. A general framework for implementing distributed local search algorithms for DisCOPs is proposed. The proposed framework makes use of a BF S-tree in order to accumulate the costs of the system’s state in its different steps and to propagate the detection of a new best step when it is found. The resulting framework enhances local search algorithms for DisCOPs with the anytime property. The proposed framework does not require additional network load. Agents are required to hold a small (linear) additional space (beside the requirements of the algorithm in use). The proposed framework preserves privacy at a higher level than complete DisCOP algorithms which make use of a pseudo-tree (ADOP T, DP OP).
Asynchronous Algorithms for Approximate Distributed Constraint Optimization with Quality Bounds
"... Distributed Constraint Optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NPhard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds o ..."
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Cited by 23 (5 self)
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Distributed Constraint Optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NPhard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds on solution quality is k-size optimality, which defines a local optimality criterion based on the size of the group of deviating agents. Unfortunately, the lack of a general-purpose algorithm and the commitment to forming groups based solely on group size has limited the use of k-size optimality. This paper introduces t-distance optimality which departs from k-size optimality by using graph distance as an alternative criteria for selecting groups of deviating agents. This throws open a new research direction into the tradeoffs between different group selection
Distributed Constraint Optimization with Structured Resource Constraints
"... Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrai ..."
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Cited by 19 (2 self)
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Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrained DCOP- has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multi-agent settings.
B.: E[DPOP]: Distributed constraint optimization under stochastic uncertainty using collaborative sampling
, 2009
"... Abstract. Many applications that require distributed optimization also include uncertainty about the problem and the optimization criteria themselves. However, current approaches to distributed optimization assume that the problem is entirely known before optimization is carried out, while approache ..."
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Cited by 16 (6 self)
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Abstract. Many applications that require distributed optimization also include uncertainty about the problem and the optimization criteria themselves. However, current approaches to distributed optimization assume that the problem is entirely known before optimization is carried out, while approaches to optimization with uncertainty have been investigated for centralized algorithms. This paper introduces the framework of Distributed Constraint Optimization under Stochastic Uncertainty (StochDCOP), in which random variables with known probability distributions are used to model sources of uncertainty. Our main novel contribution is a distributed procedure called collaborative sampling, which we use to produce several new versions of the DPOP algorithm for StochDCOPs. We evaluate the benefits of collaborative sampling over the simple approach in which each agent samples the random variables independently. We also show that collaborative sampling can be used to implement a new, distributed version of the consensus algorithm, which is a well-known algorithm for centralized, online stochastic optimization in which the solution chosen is the one that is optimal in most cases, rather than the one that maximizes the expected utility. 1
Distributed constraint optimization for large teams of mobile sensing agents
- In Proceedings of IAT
, 2009
"... Abstract. A team of mobile sensors can be used for coverage of targets in differ-ent environments. The dynamic nature of such an application requires the team of agents to adjust their locations with respect to changes which occur. The dy-namic nature is caused by environment changes, changes in the ..."
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Cited by 13 (3 self)
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Abstract. A team of mobile sensors can be used for coverage of targets in differ-ent environments. The dynamic nature of such an application requires the team of agents to adjust their locations with respect to changes which occur. The dy-namic nature is caused by environment changes, changes in the agents ’ tasks and by technology failures. A new model for representing problems of mobile sensor teams based on Dis-tributed Constraint Optimization Problems (DCOP), is proposed. The proposed model, needs to handle a dynamic problem in which the alternative assignments for agents and set of neighbors, derive from their physical location which is dy-namic. DCOP MST enables representation of variant dynamic elements which a team of mobile sensing agents face. A reputation model is used to determine the credibility of agents. By representing the dynamic sensing coverage requirements in the same scale as the agents ’ credibility, the deployment of sensors in the area can be evaluated and adjusted with correspondence to dynamic changes. In or-der to solve a DCOP MST a local (incomplete) search algorithm (MGM MST) based on the MGM algorithm is proposed and combined with various explo-ration methods. While existing exploration methods are evidently not effective in DCOP MSTs, new exploration methods which are designed for this special application are found to be successful in our experimental study. 1
Caching schemes for DCOP search algorithms
- In Proceedings of AAMAS
, 2009
"... Distributed Constraint Optimization (DCOP) is useful for solving agent-coordination problems. Any-space DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding w ..."
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Cited by 9 (5 self)
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Distributed Constraint Optimization (DCOP) is useful for solving agent-coordination problems. Any-space DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding which information to preempt from the cache when a new piece of information needs to be cached. Our contributions are three-fold: (1) We frame the problem as an optimization problem. (2) We introduce three new caching schemes (MaxPriority, MaxEffort and MaxUtility) that exploit the cached information in a DCOP-specific way. (3) We evaluate how the resulting speed up depends on the search strategy of the DCOP search algorithm. Our experimental results show that, on all tested DCOP problem classes, our MaxEffort and MaxUtility schemes speed up ADOPT (which uses best-first search) more than the other tested caching schemes, while our MaxPriority scheme speeds up BnB-ADOPT (which uses depth-first branch-andbound search) at least as much as the other tested caching schemes.
Distributed Gibbs: A Memory-Bounded Sampling-Based DCOP Algorithm
"... Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent 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 multi-agent 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) sampling-based 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 sampling-based 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.
