Asynchronous aggregation and consistency in distributed constraint satisfaction

Silaghi, Marius; Faltings, Boi

doi:10.1016/j.artint.2004.10.003

Cited by 38 publications

(40 citation statements)

References 6 publications

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“…Most of the studies of Asynchronous Backtracking used a static order of agents and variables [2,10,20,25]. An exponential space algorithm that used dynamic ordering has shown improvement in run-time over ABT [25].…”

Section: Discussionmentioning

confidence: 99%

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Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Zivan

Meisels

2005

Principles and Practice of Constraint Programming - CP 2005

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An algorithm that performs asynchronous backtracking on distributed CSPs, with dynamic ordering of agents is proposed, ABT DO. Agents propose reorderings of lower priority agents and send these proposals whenever they send assignment messages. Changes of ordering triggers a different computation of N ogoods. The dynamic ordered asynchronous backtracking algorithm uses polynomial space, similarly to standard ABT. The ABT DO algorithm with three different ordering heuristics is compared to standard ABT on randomly generated DisCSPs. A Nogoodtriggered heuristic, inspired by dynamic backtracking, is found to outperform static order ABT by a large factor in run-time and improve the network load.

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Section: Discussionmentioning

confidence: 99%

“…Asynchronous Backtracking was first presented by Yokoo [25,26] and was developed further and studied in [2,3,10,20]. Agents in the ABT algorithm perform assignments asynchronously according to their current view of the system's state.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Zivan

Meisels

2005

Principles and Practice of Constraint Programming - CP 2005

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“…Propagating any deletion means that any deleted value and its justifying nogood of any agent has to be sent to any other agent constrained with it. This was already proposed in [9,10]. Agents have to store the received nogoods while they are active, but the space complexity remains polynomial [9].…”

Section: Propagating Any Deletionmentioning

confidence: 99%

Connecting ABT with Arc Consistency

Brito

Meseguer

2008

Lecture Notes in Computer Science

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Abstract. ABT is the reference algorithm for asynchronous distributed constraint satisfaction. When searching, ABT produces nogoods as justifications of deleted values. When one of such nogoods has an empty left-hand side, the considered value is eliminated unconditionally, once and for all. This value deletion can be propagated using standard arc consistency techniques, producing new deletions in the domains of other variables. This causes substantial reductions in the search effort required to solve a class of problems. We also extend this idea to the propagation of conditional deletions, something already proposed in the past. We provide experimental results that show the benefits of the proposed approach, especially considering communication cost.

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“…There exist several common extensions to this definition of DCOPs providing for several variables per agent, and for agents that hold constraints involving only variables that they do not control. While non-trivial optimizations are possible when considering such frameworks (Yokoo & Hirayama, 1998;Silaghi & Faltings, 2004), any solution for the version discussed here can be easily applied to those cases.…”

Section: Definition 2 (Dcop)mentioning

confidence: 99%

“…However, we also provide results computed in a scenario simulating random latency in communication. Sometimes, local computation time is also factored into the evaluation metric by weighting the computation associated with each constraint check as a fraction (e.g., between one tenth and one millionth) of the latency of a message (Yokoo et al, 1992;Silaghi & Faltings, 2004;Chechetka & Sycara, 2006). The current value of this fraction for the Internet is around one thousand, estimating approximately 10 −4 seconds/constraint-check and 0.5 seconds/message.…”

Section: Introductionmentioning

confidence: 99%

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Silaghi

Yokoo

2008

Auton Agent Multi-Agent Syst

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This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints.DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement.

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Asynchronous aggregation and consistency in distributed constraint satisfaction

Cited by 38 publications

References 6 publications

Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Connecting ABT with Arc Consistency

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

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