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

Chen, Dingding; Deng, Yimin; Chen, Ziyu; He, Zhongshi; Zhang, Wenxin

doi:10.1007/s10458-020-09476-5

Cited by 6 publications

(8 citation statements)

References 30 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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“…Figure 1c shows the colored map corresponding to the Graph Coloring of 25 regions. Definition 2 (Constraint Optimization Problem (COP) [10,18]). A Constraint Optimization Problem (COP) P = P ∪ {f } where P is a CSP (see Definition 1) and f is an objective function to be optimized (either maximized or minimized).…”

Section: Example 1 (Graph Coloring) Given a Graph G = (V E) Graph mentioning

confidence: 99%

Constraint Programming for the Pandemic in Peru

Ugarte

2021

Communications in Computer and Information Science

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Currently, the world requires techniques that match infected people and hospital beds together given various criteria such as the severity of infection, patient location, hospital capacity, etc. Deep Learning might seems to be a perfect fit for this: various configurations from a broad range of parameters that need to be reduced to a few solutions. But, this models require to be trained, hence the need for historical data on previous cases leading to a waste of time would in cleaning and consolidating a dataset and lengthy training sessions need to be performed with a variety of architectures.Nevertheless, formulating this problem as a Constraint Satisfaction Problem (CSP), the aforementioned downsides will not be present while still optimal results, and without the need for any historical data. In this paper, a CSP model is used to search for the best distribution of COVID-19 patients with a severity of patients requiring hospitalization and patients requiring ICU beds, in hospitals in a part of Lima.

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“…Population-based algorithms recently emerged as a new category of incomplete algorithms through the introduction of ACO DCOP [31]. Although there are prior examples of population-based DCSP solvers such as ACO DCSP [48] and SoHC [49], they are not directly suitable for solving DCOPs.…”

Section: Related Workmentioning

confidence: 99%

“…The population-based approaches started with ACO DCOP [31] which adapts a centralized population-based approach using as Ant Colony Optimization (ACO) procedure [32]. It is worth noting that, in addition to ACO, a wide variety of other centralized population-based algorithms exist and a large number of them go under the broad heading of evolutionary op-timization techniques [33,34].…”

Section: Introductionmentioning

confidence: 99%

On Population-Based Algorithms for Distributed Constraint Optimization Problems

Mahmud¹,

Khan²,

Jennings³

2020

Preprint

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Distributed Constraint Optimization Problems (DCOPs) are a widely studied class of optimization problems in which interaction between a set of cooperative agents are modeled as a set of constraints. DCOPs are NP-hard and significant effort has been devoted to developing methods for finding incomplete solutions. In this paper, we study an emerging class of such incomplete algorithms that are broadly termed as population-based algorithms. The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid localoptima. In recent years, this class of algorithms has gained significant attention due to their ability to produce high-quality incomplete solutions. With the primary goal of further improving the quality of solutions compared to the state-of-the-art incomplete DCOP algorithms, we present two new population-based algorithms in this paper. Our first approach, Anytime Evolutionary DCOP or AED, exploits evolutionary optimization meta-heuristics to solve DCOPs. We also present a novel anytime update mechanism that gives AED its anytime property. While in our second contribution, we show that population-based approaches can be combined with local search approaches. Specifically, we develop an algorithm called DPSA based on the Simulated Annealing meta-heuristic. We empirically evaluate these two algorithms to illustrate their respective effectiveness in different settings against the state-of-the-art incomplete DCOP algorithms including all existing population-based algorithms in a wide variety of benchmarks. Our evaluation shows AED and DPSA markedly outperform the state-of-the-art and produce up to 75% improved solutions.

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

Cited by 6 publications

References 30 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

Constraint Programming for the Pandemic in Peru

On Population-Based Algorithms for Distributed Constraint Optimization Problems

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