Applying Max-sum to asymmetric distributed constraint optimization problems

Zivan, Roie; Parash, Tomer; Cohen-Lavi, Liel; Naveh, Yoav

doi:10.1007/s10458-019-09436-8

Cited by 14 publications

(14 citation statements)

References 20 publications

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“…While the original MODCOP addresses a few objectives that are shared among all agents, the concept of the multiple objectives is generalized into the preferences of individual agents. The preferences of agents also relate to Asymmetric DCOPs [9,10] where each agent differently evaluates asymmetric functions. The concept of the Asymmetric DCOP is also extended with MODCOPs where each agent differently evaluates its own local problem that represents the preference of the agent.…”

Section: Distributed Constraint Optimization Problem and Preferences mentioning

confidence: 99%

“…Asymmetric DCOP (ADCOP) [9,10] is an extended class of DCOPs, where different objective functions are asymmetrically defined for a set of agents. Based on the definition of DCOP shown in Definition 2.1, an ADCOP is generally defined as follows.…”

Section: Asymmetric Dcopmentioning

confidence: 99%

“…The goal of the problem is to optimize the summation of objective values. In the previous studies on ADCOPs, solution methods for binary asymmetric functions on pairs of agents are presented [9,10]. In the case of binary asymmetric functions, two different functions f i (x a , x b ) and f j (x a , x b ) are defined for a pair of variables x a and x b in different agents i and j.…”

Section: Asymmetric Dcopmentioning

confidence: 99%

“…After the initialization (Fig. 4, line 9), the local search is iteratively performed as multiple rounds (lines [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Each round consists of the following steps.…”

Section: Local Searchmentioning

confidence: 99%

“…g9 2: a rounded integer value based on a gamma distribution with (α = 9, β = 2), similar to [19]. u1-10: an integer value in [1,10] based on uniform distribution. Results were averaged over 25 instances of the problems.…”

Section: Example Problems and Evaluation Valuesmentioning

confidence: 99%

See 4 more Smart Citations

Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

Matsui

Silaghi

Okimoto

et al. 2018

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Distributed Constraint Optimization Problem (DCOP) has been studied as a fundamental component of multiagent systems. With DCOPs, various applications on multiagent systems are formalized as constraint optimization problems where variables and functions are distributed among agents. Leximin AMODCOP has been proposed as a class of Multiple Objective DCOPs, where multiple objectives for individual agents are optimized based on the leximin operator. This problem also relates to Asymmetric DCOPs based on its the criteria of fairness among agents. Previous studies explore only Leximin AMODCOPs on constraint graphs limited to functions with unary or binary scopes. We address the Leximin AMODCOPs on factor graphs that directly represent n-ary functions. A dynamic programming method on factor graphs is investigated as an exact solution method. In addition, for relatively dense problems, we also investigate several approximate/inexact algorithms.

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Section: Distributed Constraint Optimization Problem and Preferences mentioning

confidence: 99%

Section: Asymmetric Dcopmentioning

confidence: 99%

Section: Asymmetric Dcopmentioning

confidence: 99%

Section: Local Searchmentioning

confidence: 99%

Section: Example Problems and Evaluation Valuesmentioning

confidence: 99%

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Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

Matsui

Silaghi

Okimoto

et al. 2018

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A hybrid tree-based algorithm to solve asymmetric distributed constraint optimization problems

Chen

Deng

Chen

et al. 2020

Auton Agent Multi-Agent Syst

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Effect of asynchronous execution and imperfect communication on max-sum belief propagation

Zivan,

Rachmut,

Perry

et al. 2023

Auton Agent Multi-Agent Syst

Self Cite

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Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of these two execution versions nor the implications of imperfect communication (i.e., massage delay and message loss) on the two versions, have been investigated to the best of our knowledge. We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating the differences between the solutions generated by the two versions of the algorithm, with and without message delay or loss; and (3) Establishing both theoretically and empirically the positive effect of damping on reducing the differences between the two versions. Our results indicate that in contrast to recent published results indicating the drastic effect that message latency has on distributed local search, damped Max-sum is robust to imperfect communication.

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Applying Max-sum to asymmetric distributed constraint optimization problems

Cited by 14 publications

References 20 publications

Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

A hybrid tree-based algorithm to solve asymmetric distributed constraint optimization problems

Effect of asynchronous execution and imperfect communication on max-sum belief propagation

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