Distributed Constraint Optimization Problems: Review and perspectives

Leite, Allan Rodrigo; Enembreck, Fabrício; Barthès, Jean-Paul

doi:10.1016/j.eswa.2014.02.039

Cited by 42 publications

(33 citation statements)

References 20 publications

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“…T smp ) and PMP (i.e. T pmp ) can be generated using Equations (19) and (20), respectively. Here, the function requiredTime () takes T T T , , and an acyclic factor graph as an input, and computes the time it needs to finish the message passing by following the regulation of SMP.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…There has been some work on providing guarantees on the performance of such algorithms in larger DCOP settings [15][16][17][18]. However, for large and complex problems, consisting of hundreds or thousands of nodes, this class of algorithms often comes up with a global solution far from the optimal [19][20][21][22]. This is because agents do not explicitly communicate their utility for being in any particular state.…”

Section: Introductionmentioning

confidence: 99%

“…GDL-based inference algorithms are the other popular class of non-exact approaches [23]. Rather than exchanging self-states with their neighbours in local search, agents in a GDL-based algorithm explicitly share the consequences of choosing nonpreferred states with the preferred one during inference through the graphical representation of a DCOP [2,20,24]. Thus, agents can obtain global utilities for each possible value assignment.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, [29] demonstrates that the impact of inconsistent views is worse than the waiting time of the agents regarding the total completion time, due to the effort required to revise an action in the asynchronous protocol. Thus, the completion time for both the cases is proportional to the diameter of the factor graph, and the asynchronous version never outperforms SMP in terms of the completion time [20,27,29].…”

Section: Introductionmentioning

confidence: 99%

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Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs

Khan

Tran-Thanh

Ramchurn

et al. 2018

The Computer Journal

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This paper develops a new approach to speed up Generalized Distributive Law (GDL) based message passing algorithms that are used to solve large-scale Distributed Constraint Optimization Problems (DCOPs) in multi-agent systems. In particular, we significantly reduce computation and communication costs in terms of convergence time for algorithms such as Max-Sum, Bounded Max-Sum, Fast Max-Sum, Bounded Fast Max-Sum, BnB Max-Sum, BnB Fast Max-Sum and Generalized Fast Belief Propagation. This is important since it is often observed that the outcome obtained from such algorithms becomes outdated or unusable if the optimization process takes too much time. Specifically, the issue of taking too long to complete the internal operation of a DCOP algorithm is even more severe and commonplace in a system where the algorithm has to deal with a large number of agents, tasks and resources. This, in turn, limits the practical scalability of such algorithms. In other words, an optimization algorithm can be used in larger systems if the completion time can be reduced. However, it is challenging to maintain the solution quality while minimizing the completion time. Considering this trade-off, we propose a generic message passing protocol for GDL-based algorithms that combines clustering with domain pruning, as well as the use of a regression method to determine the appropriate number of clusters for a given scenario. We empirically evaluate the performance of our method in a number of settings and find that it brings down the completion time by around 37-85% (1.6-6.5 times faster) for 100-900 nodes, and by around 47-91% (1.9-11 times faster) for 3000-10 000 nodes compared to the current state-of-the-art.

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Section: Empirical Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs

Khan

Tran-Thanh

Ramchurn

et al. 2018

The Computer Journal

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show abstract

“…Like in [25] our algorithm is based on a two steps approach: (i) first, the robots plan several paths individually using the RRT algorithm, (ii) second, the robots communicate asynchronously with their neighbors as needed to find the set of paths that minimize an user-defined utility function. This second step is realized by formulating the problem as a distributed constrained optimization problem [12].…”

Section: Introductionmentioning

confidence: 99%

An asynchronous distributed constraint optimization approach to multi-robot path planning with complex constraints

Viseras

Karolj

Merino

2017

Proceedings of the Symposium on Applied Computing

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Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multirobot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) [15] is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots.

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Distributed Constrained Optimization Towards Effective Agent-Based Microgrid Energy Resource Management

Lezama

Cote

Farinelli

et al. 2019

Progress in Artificial Intelligence

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The current energy scenario requires actions towards the reduction of energy consumption and the use of renewable resources. In this context, a microgrid is a self-sustained network that can operate connected to the smart grid or in isolation. The long-term scheduling of on/off cycles of devices is a critical problem that has been commonly addressed by centralized approaches. In this work, we propose a novel agent-based method to solve the long-term scheduling problem as a distributed constraint optimization problem (DCOP) by modelling future system configurations rather than reacting to changes. Moreover, with respect to approaches based on decentralised reinforcement learning, we can directly encode system-wide hard constraints (such as for example the Kirchhoff law) which are not easy to represent in a factored representation of the problem. We compare different multi-agent DCOP algorithms showing that the proposed method can find optimal/near-optimal solutions for a specific case study.

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Distributed Constraint Optimization Problems: Review and perspectives

Cited by 42 publications

References 20 publications

Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs

Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs

An asynchronous distributed constraint optimization approach to multi-robot path planning with complex constraints

Distributed Constrained Optimization Towards Effective Agent-Based Microgrid Energy Resource Management

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