A decentralized variable ordering method for distributed constraint optimization

Chechetka, Anton; Sycara, Katia

doi:10.1145/1082473.1082746

Cited by 6 publications

(3 citation statements)

References 4 publications

(3 reference statements)

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“…Once, all information about the constraint graph is gathered by the system agent, it can perform a centralized algorithm to build the pseudo-tree ordering. A decentralized modification of the procedure for building the pseudo-tree was introduced by Chechetka and Sycara in [8]. This algorithm allows the distributed construction of pseudo-trees without needing to deliver any global information about the whole problem to a single agent.…”

Section: Level 1 Levelmentioning

confidence: 99%

Nogood-based asynchronous forward checking algorithms

Wahbi¹,

Ezzahir²,

Bessière³

et al. 2013

Constraints

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We propose two new algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). Besides its use of nogoods as justification of value removals, AFC-ng allows simultaneous backtracks going from different agents to different destinations. The second algorithm, Asynchronous Forward Checking Tree (AFCtree), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-tree runs simultaneous search processes in disjoint problem subtrees and exploits the parallelism inherent in the problem. We prove that AFC-ng and AFC-tree only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisCSPs and instances from real benchmarks: sensor networks and distributed meeting scheduling. Our experiments show that AFC-ng improves on AFC and that AFC-tree outperforms all compared algorithms, particularly on sparse problems.

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Section: Level 1 Levelmentioning

confidence: 99%

Nogood-based asynchronous forward checking algorithms

Wahbi¹,

Ezzahir²,

Bessière³

et al. 2013

Constraints

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“…The extension of ABT called ABTR (Silaghi, SamHaroud, & Faltings, 2001a;Silaghi, 2006) proposes a way to extend ABT-based algorithms to allow for dynamic ordering of the agents (Armstrong & Durfee, 1997). Work in the area consistent with this approach, but mainly favoring static ordering, appears in (Liu & Sycara, 1995;Chechetka & Sycara, 2005). Finding good heuristics was shown to be a difficult problem (Silaghi et al, 2001b;Zivan & Meisels, 2005) and here one will need to take into account the importance of the existence of a short DFS tree compatible to the current ordering.…”

Section: Possible Extensionsmentioning

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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“…Finally, note that these relaxations can be performed in a distributed manner. The priority tree can be created using a decentralized algorithm (Chechetka and Sycara 2005). Then, using only knowledge of their own priority and the priority of their neighbours, agents can remove the necessary inter-agent constraints.…”

Section: Relaxations In Adoptmentioning

confidence: 99%

Using relaxations to improve search in distributed constraint optimisation

Burke

Brown

2007

Artif Intell Rev

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Densely connected distributed constraint optimisation problems (DisCOP) can be difficult to solve optimally, but finding good lower bounds on constraint costs can help to speed up search. We show how good lower bounds can be found by solving relaxed problems obtained by removing inter-agent constraints. We present modifications to the Adopt DisCOP algorithm that allow an arbitrary number of relaxations to be performed prior to solving the original problem. We identify useful relaxations based on the solving structure used by Adopt , and demonstrate that when these relaxations are incorporated as part of the search it can lead to significant performance improvements. In particular, where agents have significant local constraint costs, we achieve over an order of magnitude reduction in messages exchanged between agents. Finally, we identify cases where such relaxation techniques produce less consistent benefits.

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A decentralized variable ordering method for distributed constraint optimization

Cited by 6 publications

References 4 publications

Nogood-based asynchronous forward checking algorithms

Nogood-based asynchronous forward checking algorithms

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Using relaxations to improve search in distributed constraint optimisation

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