Asynchronous Forward Bounding for Distributed COPs

Gershman, Amir; Meisels, Amnon; Zivan, Roie

doi:10.1613/jair.2591

Cited by 99 publications

(96 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, we compare ASP-DPOP against the original DPOP as well as other three implementations of complete DCOP solvers: Asynchronous Forward-Bounding (AFB), Hard Constraint-DPOP (H-DPOP), and Open-DPOP (ODPOP). AFB (Gershman et al 2009) is a complete search-based algorithm to solve DCOPs. H-DPOP (Kumar et al 2008) is a complete DCOP solver that, in addition, propagates hard constraints to prune the search space.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Solving distributed constraint optimization problems using logic programming

Son

Pontelli

et al. 2017

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) It shows how one can formulate DCOPs as logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) It demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The asynchronous forward-bounding algorithm (AFB) (Gershman et al 2009), to the best of our knowledge, is the most recent complete search-based algorithm to solve DCOPs. AFB makes use of a Branch and Bound scheme to identify a complete value assignment that minimizes the aggregate utility of all constraints.…”

Section: Background On Afbmentioning

confidence: 99%

Solving distributed constraint optimization problems using logic programming

Son

Pontelli

et al. 2017

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…The Distributed Constraint Satisfaction (DisCSP) (Yokoo, Durfee, Ishida, & Kuwabara, 1992;Sycara, Roth, Sadeh-Koniecpol, & Fox, 1991;Collin, Dechter, & Katz, 1991Solotorevsky, Gudes, & Meisels, 1996) and the Distributed Constraint Optimization (DCOP) Zhang & Wittenburg, 2003;Petcu & Faltings, 2005b;Gershman, Meisels, & Zivan, 2006) formalisms were introduced to enable distributed solutions. The agents involved in such problems must communicate with each other to find a solution to the overall problem (unknown to any one of them).…”

Section: Constraint Satisfaction and Optimizationmentioning

confidence: 99%

M-DPOP: Faithful Distributed Implementation of Efficient Social Choice Problems

Petcu¹,

Faltings²,

Parkes³

2008

jair

View full text Add to dashboard Cite

In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce M-DPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report information, and send messages that is in its own best interest. Determining agent i's payment requires solving the social choice problem without agent i. Here, we present a method to reuse computation performed in solving the main problem in a way that is robust against manipulation by the excluded agent. Experimental results on structured problems show that as much as 87% of the computation required for solving the marginal problems can be avoided by re-use, providing very good scalability in the number of agents. On unstructured problems, we observe a sensitivity of M-DPOP to the density of the problem, and we show that reusability decreases from almost 100% for very sparse problems to around 20% for highly connected problems. We close with a discussion of the features of DCOP that enable faithful implementations in this problem, the challenge of reusing computation from the main problem to marginal problems in other algorithms such as ADOPT and OptAPO, and the prospect of methods to avoid the welfare loss that can occur because of the transfer of payments to the bank.

show abstract

“…This can be done with the introduction of valued consistency nogoods. Details and variations are described in (Silaghi, 2002(Silaghi, , 2003bGershman, Meisels, & Zivan, 2006, 2007Sultanik et al, 2006). The control of the space requirements for such extensions may be based on the use of consistency nogoods to simulate the distributed weighted arc consistency in , while the maintenance of this control of space in asynchronous search may be similar to the one for distributed CSPs described in (Silaghi & Faltings, 2004).…”

Section: Possible Extensionsmentioning

confidence: 99%

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Silaghi

Yokoo

2008

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

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.

show abstract

Asynchronous Forward Bounding for Distributed COPs

Cited by 99 publications

References 30 publications

Solving distributed constraint optimization problems using logic programming

Solving distributed constraint optimization problems using logic programming

M-DPOP: Faithful Distributed Implementation of Efficient Social Choice Problems

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Contact Info

Product

Resources

About