Explorative anytime local search for distributed constraint optimization

Zivan, Roie; Okamoto, Steven; Peled, Hilla

doi:10.1016/j.artint.2014.03.002

Cited by 65 publications

(59 citation statements)

References 15 publications

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“…An algorithm is said anytime if it can return a valid solution even if the DCOP agents are interrupted at any time before the algorithm terminates. Anytime algorithms are expected to seek for solutions of increasing quality as they keep running (Zivan, Okamoto, & Peled, 2014).…”

Section: Algorithmsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

137

119

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The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments.This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

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Section: Algorithmsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

137

119

View full text Add to dashboard Cite

show abstract

“…Agents in DCOPs need to coordinate value assignments of their variables, in a decentralized and distributed manner, to optimize their objective functions. Researchers have used DCOPs to model various multiagent coordination and resource allocation problems (Maheswaran et al 2004;Zivan, Glinton, and Sycara 2009;Zivan, Okamoto, and Peled 2014;Lass et al 2008;Kumar, Faltings, and Petcu 2009;Ueda, Iwasaki, and Yokoo 2010;Léauté and Faltings 2011).…”

Section: Introductionmentioning

confidence: 99%

Multi-Context System for Optimization Problems

Son

Pontelli

2019

AAAI

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This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.

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“…ADOPT (Modi et al ) performs a tree search with memory‐bounded dynamic programming. On the other hand, several incomplete methods are based on local search approach (Zivan ; Vinyals et al ).…”

Section: Introductionmentioning

confidence: 99%

“…The Distributed Constraint Optimization Problem (DCOP) lies at the foundations of multiagent cooperation (Modi et al 2005;Petcu and Faltings 2005;Farinelli et al 2008;Zivan 2008). With DCOPs, the optimization in distributed resource allocation including distributed sensor networks (Zhang et al 2005), meeting scheduling (Maheswaran et al 2004), disaster response (Ramchurn et al 2010), and smart grids (Miller et al 2012) is formalized using constraint optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Leximin Asymmetric Multiple Objective Distributed Constraint Optimization Problem

Matsui

Matsuo

Silaghi

et al. 2017

Computational Intelligence

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The Distributed Constraint Optimization Problem (DCOP) lies at the foundations of multiagent cooperation. With DCOPs, the optimization in distributed resource allocation problems is formalized using constraint optimization problems. The solvers for the problem are designed based on decentralized cooperative algorithms that are performed by multiple agents. In a conventional DCOP, a single objective is considered. The Multiple Objective Distributed Constraint Optimization Problem (MODCOP) is an extension of the DCOP framework, where agents cooperatively have to optimize simultaneously multiple objective functions. In the conventional MODCOPs, a few objectives are globally defined and agents cooperate to find the Pareto optimal solution. However, such models do not capture the interests of each agent. On the other hand, in several practical problems, the share of each agent is important. Such shares are modeled as preference values of agents. This class of problems can be defined using the MODCOP on the preferences of agents. In particular, we define optimization problems based on leximin ordering and Asymmetric DCOPs (Leximin AMODCOPs). The leximin defines an ordering among vectors of objective values. In addition, Asymmetric DCOPs capture the preferences of agents. Because the optimization based on the leximin ordering improves the equality among the satisfied preferences of the agents, this class of problems is important. We propose several solution methods for Leximin AMODCOPs generalizing traditional operators into the operators on sorted objective vectors and leximin. The solution methods applied to the Leximin AMODCOPs are based on pseudo trees. Also, the investigated search methods employ the concept of boundaries of the sorted vectors.

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Explorative anytime local search for distributed constraint optimization

Cited by 65 publications

References 15 publications

Distributed Constraint Optimization Problems and Applications: A Survey

Distributed Constraint Optimization Problems and Applications: A Survey

Multi-Context System for Optimization Problems

Leximin Asymmetric Multiple Objective Distributed Constraint Optimization Problem

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