Railroad Driving Model Based on Distributed Constraint Optimization

Leite, Allan Rodrigo; Giacomet, Bruno; Enembreck, Fabrício

doi:10.1109/wi-iat.2009.197

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…The distributed constraint optimization problem (DCOP) provides a powerful formalism for modeling many decentralized coordination tasks in multiagent systems, and is widely used in realistic applications, such as sensor networks (Farinelli, Rogers, & Jennings, 2014;Rust, Picard, & Ramparany, 2016), task scheduling (Maheswaran, Tambe, Bowring, Pearce, & Varakantham, 2004b;Leite, Giacomet, & Enembreck, 2009), resource allocation (Carpenter et al, 2007;Amigoni, Castelletti, & Giuliani, 2015), among others. However, because DCOP has been shown to be an NP-Hard problem (Modi, Shen, Tambe, & Yokoo, 2005), some applications are unsuitable for applying this formalism, owing to a lack of efficient DCOP algorithms especially on hard instances of DCOP involving large-scale, complex domains.…”

Section: Introductionmentioning

confidence: 99%

Using Collective Behavior of Coupled Oscillators for Solving DCOP

Leite¹,

Enembreck²

2019

jair

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The distributed constraint optimization problem (DCOP) has emerged as one of the most promising coordination techniques in multiagent systems. However, because DCOP is known to be NP-hard, the existing DCOP techniques are often unsuitable for large-scale applications, which require distributed and scalable algorithms to deal with severely limited computing and communication. In this paper, we present a novel approach to provide approximate solutions for large-scale, complex DCOPs. This approach introduces concepts of synchronization of coupled oscillators for speeding up the convergence process towards high-quality solutions. We propose a new anytime local search DCOP algorithm, called Coupled Oscillator OPTimization (COOPT), which amounts to iteratively solving a DCOP by agents exchanging local information that brings them to a consensus. We empirically evaluate COOPT on constraint networks involving hundreds of variables with different topologies, domains, and densities. Our experimental results demonstrate that COOPT outperforms other incomplete state-of-the-art DCOP algorithms, especially in terms of the agents' communication cost and solution quality.

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Section: Introductionmentioning

confidence: 99%

Using Collective Behavior of Coupled Oscillators for Solving DCOP

Leite¹,

Enembreck²

2019

jair

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“…As a promising framework for MAS problems, Distributed Constraint Optimization Problem (DCOP) has become a research hotspot in artificial intelligence area and widely deployed in real life such as distributed task planning [1], meeting scheduling [2] resource allocation [3], sensor network [4] and so on. A DCOP is composed of a number of agents, each responsible for choosing a value from its finite domain for its own variable which is subject to some constraints.…”

Section: Introductionmentioning

confidence: 99%

Layering-ADOPT: ADOPT with Layering Boundary

He¹,

Chen²,

He³

2017

dtetr

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ADOPT and BnB-ADOPT are the two important search-based complete algorithms to solve Distributed Constraint Optimization Problem (DCOP). However, a large number of solution reconstructions appear in ADOPT and sub-optimal branch can't be promptly pruned in BnB-ADOPT. A layering DCOP algorithm to combine ADOPT and BnB-ADOPT is proposed to remedy their weakness, named Layering-ADOPT. In Layering-ADOPT, a layering boundary is introduced to divide all agents into two classes, one performing ADOPT and another for BnB-ADOPT. This paper presents a rule to get a layering boundary and a new strategy to realize the joint between ADOPT and BnB-ADOPT. In the experiment, Layering-ADOPT and ADOPT as well as BnB-ADOPT are compared on random DCOPs. The results show that Layering-ADOPT is superior to ADOPT and BnB-ADOPT on the test problems.

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BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

Chen

et al. 2017

Artif Intell Rev

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Railroad Driving Model Based on Distributed Constraint Optimization

Cited by 12 publications

References 15 publications

Using Collective Behavior of Coupled Oscillators for Solving DCOP

Using Collective Behavior of Coupled Oscillators for Solving DCOP

Layering-ADOPT: ADOPT with Layering Boundary

BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

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