Distributed Gibbs: A Linear-Space Sampling-Based DCOP Algorithm

Nguyen, Duc Thien; Yeoh, William; Lau, Hoong Chuin; Zivan, Roie

doi:10.1613/jair.1.11400

Cited by 41 publications

(37 citation statements)

References 35 publications

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“…Some representative examples of the first group are SyncBB [41], ADOPT [61], DPOP [74], AFB [34], BnB-ADOPT [96], and PT-FB [52]. Some recent examples of the non-exact algorithms are GDBA [67], BMS [80], BnB-FMS [53], Max-Sum-ADVP [102], D-Gibbs [64], ACO-DCOP [13], and AED [55].…”

Section: Related Literaturementioning

confidence: 99%

A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

Farhadi

Jennings

2021

SN COMPUT. SCI.

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Distributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.

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Section: Related Literaturementioning

confidence: 99%

A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

Farhadi

Jennings

2021

SN COMPUT. SCI.

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“…However, complete algorithms scale poorly and are unsuitable for large real-world applications. Therefore, considerable research efforts have been devoted to develop incomplete algorithms that trade the solution quality for smaller computational overheads, including local search (Maheswaran, Pearce, and Tambe 2004;Okamoto, Zivan, and Nahon 2016;Zhang et al 2005), belief propagation (Cohen, Galiki, and Zivan 2020;Farinelli et al 2008;Rogers et al 2011;Zivan et al 2017;Chen et al 2018) and sampling (Nguyen et al 2019;Ottens, Dimitrakakis, and Faltings 2017).…”

Section: Introductionmentioning

confidence: 99%

Pretrained Cost Model for Distributed Constraint Optimization Problems

Deng¹,

Kong²,

An³

2021

Preprint

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Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple autonomous agents. Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics. However, existing ML-based heuristic methods are often not generalizable to different search algorithms. Most importantly, these methods usually require full knowledge about the problems to be solved, which are not suitable for distributed settings where centralization is not realistic due to geographical limitations or privacy concerns. To address the generality issue, we propose a novel directed acyclic graph representation schema for DCOPs and leverage the Graph Attention Networks (GATs) to embed graph representations. Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to construct effective heuristics to boost a broad range of DCOP algorithms where evaluating the quality of a partial assignment is critical, such as local search or backtracking search. Furthermore, to enable decentralized model inference, we propose a distributed embedding schema of GAT-PCM where each agent exchanges only embedded vectors, and show its soundness and complexity. Finally, we demonstrate the effectiveness of our model by combining it with a local search or a backtracking search algorithm. Extensive empirical evaluations indicate that the GAT-PCM-boosted algorithms significantly outperform the state-of-the-art methods in various benchmarks. Our pretrained cost model is available at https://github.com/dyc941126/GAT-PCM.

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“…Complete algorithms (Hirayama and Yokoo 1997;Modi et al 2005;Petcu and Faltings 2005;Yeoh, Felner, and Koenig 2008;Vinyals, Rodriguez-Aguilar, and Cerquides 2009;Gershman, Meisels, and Zivan 2009) can get the optimal solutions but incur exponential communication or computation overheads since DCOPs are NP-hard. In contrast, incomplete algorithms (Maheswaran, Pearce, and Tambe 2004;Arshad and Silaghi 2004;Zhang et al 2005;Ottens, Dimitrakakis, and Faltings 2012;Nguyen, Yeoh, and Lau 2013;Okamoto, Zivan, and Nahon 2016) trade accuracy for computation time and memory so that they can be applied to large-scale problems. As a kind of incomplete algorithms based on belief propagation, Max-Sum (Farinelli et al 2008) and its variants (Rogers et al 2011;Zivan and Peled 2012;Chen, Deng, and Wu 2017) have drawn a lot of attention since they can easily be deployed to any DCOP setting.…”

Section: Introductionmentioning

confidence: 99%

A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

Chen

Jiang

Deng

et al. 2019

AAAI

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Belief propagation approaches, such as Max-Sum and its variants, are important methods to solve large-scale Distributed Constraint Optimization Problems (DCOPs). However, for problems with n-ary constraints, these algorithms face a huge challenge since their computational complexity scales exponentially with the number of variables a function holds. In this paper, we present a generic and easy-touse method based on a branch-and-bound technique to solve the issue, called Function Decomposing and State Pruning (FDSP). We theoretically prove that FDSP can provide monotonically non-increasing upper bounds and speed up belief propagation based incomplete DCOP algorithms without an effect on solution quality. Also, our empirically evaluation indicates that FDSP can reduce 97% of the search space at least and effectively accelerate Max-Sum, compared with the state-of-the-art.

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Distributed Gibbs: A Linear-Space Sampling-Based DCOP Algorithm

Cited by 41 publications

References 35 publications

A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

Pretrained Cost Model for Distributed Constraint Optimization Problems

A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

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