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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
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
Incremental DCOP search algorithms for solving dynamic DCOPs (Extended Abstract
 In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS
, 2011
"... Distributed constraint optimization problems (DCOPs) are wellsuited for modeling multiagent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to ..."
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Cited by 4 (2 self)
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Distributed constraint optimization problems (DCOPs) are wellsuited for modeling multiagent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by anyspace ADOPT and anyspace BnBADOPT to find costminimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper bounds from the previous DCOP when solving the next one in the sequence. Our experimental results show that the speedup gained from this procedure increases with the amount of memory the agents have available.
Eighth International Conference on Autonomous Agents and MultiAgent Systems
, 2009
"... AAMAS 2009 included a doctoral mentoring program intended for PhD students in advanced stages of their research. The program provided an opportunity for students to interact closely with established researchers in their fields, to receive feedback on their work and to get advice on managing their ca ..."
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AAMAS 2009 included a doctoral mentoring program intended for PhD students in advanced stages of their research. The program provided an opportunity for students to interact closely with established researchers in their fields, to receive feedback on their work and to get advice on managing their careers. Specifically, the goals of the program were: • To match each student with an established researcher in the community (who will act as a mentor). • To allow students an opportunity to present their work to a friendly audience of other students as well as mentors. • To provide students with contacts and professional networking opportunities. The doctoral mentoring program afforded mentors and their students opportunities for interactions prior to the conference, as well as a oneday doctoral symposium on the first day of the conference.
Towards Scaling Up Search Algorithms for Solving Distributed Constraint Optimization Problems (Extended Abstract)
"... My thesis will demonstrate that distributed constraint optimization (DCOP) search algorithms can be scaled up ( = applied to larger problems) by applying the knowledge gained from centralized search algorithms. 1. ..."
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My thesis will demonstrate that distributed constraint optimization (DCOP) search algorithms can be scaled up ( = applied to larger problems) by applying the knowledge gained from centralized search algorithms. 1.
Incremental DCOP Search Algorithms for Solving Dynamic DCOP Problems
"... Abstract—Distributed constraint optimization (DCOP) problems are wellsuited for modeling multiagent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. I ..."
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Abstract—Distributed constraint optimization (DCOP) problems are wellsuited for modeling multiagent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOPs and (b) an incremental pseudotree construction algorithm that can be used by DCOP algorithms such as anyspace ADOPT and anyspace BnBADOPT to solve DDCOPs. Due to the incremental reasoning employed, our experimental results show that anyspace ADOPT and anyspace BnBADOPT are up to 42 % and 38 % faster, respectively, with the incremental procedure and the incremental pseudotree reconstruction algorithm than without them. I.
Incremental DCOP Search Algorithms for Solving Dynamic DCOP Problems
"... Abstract—Distributed constraint optimization (DCOP) problems are wellsuited for modeling multiagent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. I ..."
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Abstract—Distributed constraint optimization (DCOP) problems are wellsuited for modeling multiagent coordination problems. However, it only models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) model to model dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOP problems and (b) an incremental pseudotree construction algorithm that can be used by DCOP algorithms such as anyspace ADOPT and anyspace BnBADOPT to solve DDCOP problems. Due to the incremental reasoning employed, our experimental results show that anyspace ADOPT and anyspace BnBADOPT are up to 42 % and 38 % faster, respectively, with the incremental procedure and the incremental pseudotree reconstruction algorithm than without them. I.
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.
Balancing Local Resources and Global Goals in MultiplyConstrained DCOP
, 2010
"... Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. Yet, these resource co ..."
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Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. Yet, these resource constraints may need to be kept private. Designing DCOP algorithms for these domains requires managing complex tradeoffs in completeness, scalability, privacy and efficiency. This article defines the multiplyconstrained DCOP (MCDCOP) framework and provides complete (globally optimal) and incomplete (locally optimal) algorithms for solving MCDCOP problems. Complete algorithms find the best allocation of scarce resources while optimizing the team objective, while incomplete algorithms are more scalable. The algorithms use four main techniques: (i) transforming constraints to maintain privacy; (ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds; and (iv) using a virtual assignment to flag problems rendered unsatisfiable by resource constraints. Proofs of correctness are presented for all algorithms. Experimental results illustrate the strengths and weaknesses of both the complete and incomplete algorithms.