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BnBADOPT: An asynchronous branchandbound DCOP algorithm
 In Proceedings of AAMAS
, 2008
"... Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memoryboun ..."
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Cited by 64 (21 self)
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Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus 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 DCOPs and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOPs. 1
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 29 (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.
Distributed problem solving
 AI Magazine
, 2012
"... Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives ..."
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Cited by 17 (13 self)
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Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives have been applied such that the individual agent rewards are aligned with the team reward. We illustrate the motivations for distributed problem solving with an example. Imagine a decentralized channelallocation problem in a wireless local area network (WLAN), where each access point (agent) in the WLAN needs to allocate itself a channel to broadcast such that no two access points with overlapping broadcast regions (neighboring agents) are allocated the same channel to avoid interference. Figure 1 shows example mobile WLAN access points, where each access point is a Create robot fitted with a wireless CenGen radio card. Figure 2a shows an illustration of such a problem with three access points in a WLAN, where each oval ring represents the broadcast region of an access point. This problem can, in principle, be solved with a centralized approach by having each and every agent transmit all the relevant information, that is, the set of possible channels that the agent can allocate itself and its set of neighboring agents, to a centralized server. However, this centralized approach may incur unnecessary communication cost compared to a distrib
A Quantified Distributed Constraint Optimization Problem ABSTRACT
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Enforcing Soft Arc Consistency on DCOPs with Multiple Variables per Agent
"... Abstract. While most of searchbased algorithms for optimal DCOP solving assume a single variable per agent, many DCOP instances could be more appropriately modeled with several variables per agent. There are several approaches to transform a DCOP instance with several variables per agent into ano ..."
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Abstract. While most of searchbased algorithms for optimal DCOP solving assume a single variable per agent, many DCOP instances could be more appropriately modeled with several variables per agent. There are several approaches to transform a DCOP instance with several variables per agent into another instance with one variable per agent, on which existing algorithms could be applied. We present a hybrid approach that combines two of these transformations. Interestingly, our method can be connected with enforcing soft arc consistency during search, a technique that has been shown beneficial for searchbased DCOP algorithms. Using BnBADOPT+ as the solving algorithm, preliminary experimental results on DCOP random instances indicate that this hybrid approach provides clear advantages over the two transformation approaches taken in isolation, and confirm also the benefits of soft arc consistency enforcement in this context. 1
Acknowledgements
, 2010
"... First and foremost, I would like to thank my advisor, Sven Koenig, for his guidance and support throughout this journey, as well as the other members of my committee, ..."
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First and foremost, I would like to thank my advisor, Sven Koenig, for his guidance and support throughout this journey, as well as the other members of my committee,
Information Systems Engineering,
"... Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP ..."
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Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memorybounded and asynchronous algorithms. We introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP search algorithm that uses the messagepassing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memorybounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT finds costminimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memorybounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find boundederror solutions for DCOP problems within a reasonable amount of time since finding costminimal solutions is NPhard. The existing boundederror approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new boundederror approximation mechanisms that allow for relative error bounds and implement them on top of BnBADOPT. 1.
Towards CSPbased mission dispatching in C2/C4I systems
"... Abstract — One challenging problem in disaster response is to efficiently assign resources such as fire fighters and trucks to local incidents that are spatially distributed on a map. Existing systems for command and control (C2/C4I) are coming with powerful interfaces enabling the manual assignmen ..."
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Abstract — One challenging problem in disaster response is to efficiently assign resources such as fire fighters and trucks to local incidents that are spatially distributed on a map. Existing systems for command and control (C2/C4I) are coming with powerful interfaces enabling the manual assignment of resources to the incident commander. However, with increasing number of local incidents over time the performance of manual methods departs arbitrarily from an optimal solution. In this paper we introduce preliminary results of building an interface between existing professional C2/C4I systems and Constraint Satisfaction Problem (CSP)solvers. We show by using an example the feasibility of scheduling and assigning missions having deadlines and resource constraints.