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Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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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.
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 Decentralized Variable Ordering Method for Distributed Constraint Optimization
 IN PROCEEDINGS OF THE FORTH INTERNATIONAL JOINT CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS
, 2005
"... Many different multiagent problems, such as distributed scheduling can be formalized as distributed constraint optimization. Ordering the constraint variables is an important preprocessing step of the ADOPT algorithm [1], the state of the art method of solving distributed constraint optimization pr ..."
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Cited by 13 (1 self)
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Many different multiagent problems, such as distributed scheduling can be formalized as distributed constraint optimization. Ordering the constraint variables is an important preprocessing step of the ADOPT algorithm [1], the state of the art method of solving distributed constraint optimization problems (DCOP). Currently ADOPT uses depthfirst search (DFS) trees for that purpose. For certain classes of tasks DFS ordering does not exploit the problem structure as compared to pseudotree ordering [2]. Also the variables are currently ordered in a centralized manner, which requires global information about the problem structure. We present a variable ordering algorithm, which is both decentralized and makes use of pseudotrees, thus exploiting the problem structure when possible. This allows to apply ADOPT to domains, where global information is unavailable, and find solutions more efficiently. The worstcase pseudotree depth resulting from our algorithm is # 2kn, where n is the number of variables, and k is maximum block size in constraint graph. The algorithm has space complexity of O(kn) and time complexity of O(n+ ), where E is the set of edges in a constraint graph.
Robust Distributed Constraint Reasoning
"... Abstract. Distributed constraint reasoning (DCR) has recently generated much interest due to its ability to solve many real world problems without centralizing all of the information. Many DCR algorithms, however, are prone to failure if even a single agent fails, creating a situation with not only ..."
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Cited by 1 (0 self)
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Abstract. Distributed constraint reasoning (DCR) has recently generated much interest due to its ability to solve many real world problems without centralizing all of the information. Many DCR algorithms, however, are prone to failure if even a single agent fails, creating a situation with not only a central point of failure, but with npoints of failure! There are three main contributions of this work. First, we define the robust DCR problem space in terms of communications failures, agent failures and observability of failed agents. Then we describe two new types of algorithm modifications and show where they and other algorithms fit into this problem space. Finally, we analyze these algorithms and discuss what future work is needed in this area. 1
tributed Artificial Intelligence General Terms Algorithms, Experimentation
"... 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. ..."
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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.
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.
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.