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检索条件"主题词=distributed constraint optimization"

共 87 条记录，以下是51-60 订阅

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A constraint optimization Based Virtual Network Mapping Method

A Constraint Optimization Based Virtual Network Mapping Meth...

引用

4th International Conference on Graphic and Image Processing (ICGIP)

作者： Li, Xiaoling Guo, Changguo Wang, Huaimin Li, Zhendong Yang, Zhiwen Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9780819495662

Virtual network mapping problem, maps different virtual networks onto the substrate network is an extremely challenging work. This paper proposes a constraint optimization based mapping method for solving virtual network mapping problem. This method divides the problem into two phases, node mapping phase and link mapping phase, which are all NP-hard problems. Node mapping algorithm and link mapping algorithm are proposed for solving node mapping phase and link mapping phase, respectively. Node mapping algorithm adopts the thinking of greedy algorithm, mainly considers two factors, available resources which are supplied by the nodes and distance between the nodes. Link mapping algorithm is based on the result of node mapping phase, adopts the thinking of distributed constraint optimization method, which can guarantee to obtain the optimal mapping with the minimum network cost. Finally, simulation experiments are used to validate the method, and results show that the method performs very well.

关键词： virtual network mapping node mapping phase link mapping phase substrate network virtual network distributed constraint optimization

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Modeling Microgrid Islanding Problems as DCOPs

Modeling Microgrid Islanding Problems as DCOPs

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45th North American Power Symposium (NAPS)

作者： Gupta, Saurabh Yeoh, William Pontelli, Enrico Jain, Palak Ranade, Satish J. New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA

ISBN: (纸本)9781479912551

In this paper, we formulate the microgrid islanding problem as distributed constraint optimization problem (DCOP) and investigate the feasibility of solving it using off-the-shelf DCOP algorithms. This paper puts forward the potential of distributed constraint reasoning paradigm as a candidate for solving common microgrids problems.

关键词： Microgrid Islanding distributed constraint optimization

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A Study on Cooperative Action Selection Considering Unfairness in Decentralized Multiagent Reinforcement Learning 9

A Study on Cooperative Action Selection Considering Unfairne...

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9th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Matsui, Toshihiro Matsuo, Hiroshi Nagoya Inst Technol Showa Ku Gokisyo Cho Nagoya Aichi 4668555 Japan

ISBN: (纸本)9789897582196

Reinforcement learning has been studied for cooperative learning and optimization methods in multiagent systems. In several frameworks of multiagent reinforcement learning, the system's whole problem is decomposed into local problems for agents. To choose an appropriate cooperative action, the agents perform an optimization method that can be performed in a distributed manner. While the conventional goal of the learning is the maximization of the total rewards among agents, in practical resource allocation problems, unfairness among agents is critical. In several recent studies of decentralized optimization methods, unfairness was considered a criterion. We address an action selection method based on leximin criteria, which reduces the unfairness among agents, in decentralized reinforcement learning. We experimentally evaluated the effects and influences of the proposed approach on classes of sensor network problems.

关键词： Multiagent System Reinforcement Learning distributed constraint optimization Unfairness Leximin

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distributed Cooperative optimization on Cluster Trees

Distributed Cooperative Optimization on Cluster Trees

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12th IEEE/WIC/ACM International Joint Conference on Intelligent Agent Technology (IAT)

作者： Matsui, Toshihiro Matsuo, Hiroshi Nagoya Inst Technol Showa Ku Nagoya Aichi 4668555 Japan

ISBN: (纸本)9781479929023

Resource allocation problems on resource supply networks are formalized with distributed constraint optimization Problems. In previous studies, solution methods based on pseudo trees have been proposed. However, when the pseudo trees contain nodes of high degree and a large number of cycles, those methods are not applicable due to the large size of local problems in agents. Here, we employ cluster trees that hierarchically divide the problem into sub-problems. With optimistic approximation, solution methods on the cluster tree are applied to several large problems.

关键词： distributed constraint optimization resource allocation resource supply network

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Network-Guided Multi-Robot Path Planning in Discrete Representations

Network-Guided Multi-Robot Path Planning in Discrete Represe...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Luna, Ryan Bekris, Kostas E. Univ Nevada Comp Sci & Engn Dept Reno NV 89557 USA

ISBN: (纸本)9781424466757

This work deals with problems where multiple robots move on a roadmap guided by wireless nodes that form a communication network. The nodes compute paths for the robots within their communication range given information about robots only in their vicinity and communicating only with neighbors. The objective is to compute paths that are collision-free, minimize the occurrence of deadlocks, as well as the time it takes to reach the robots' goals. This paper formulates this challenge as a distributed constraint optimization problem. This formulation lends itself to a message-passing solution that guarantees collision-avoidance despite only local knowledge of the world by the network nodes. Simulations on benchmarks that cannot be solved with coupled or simple decoupled schemes are used to evaluate parameters and study the scalability, path quality and computational overhead of the approach.

关键词： collision avoidance collision avoidance discrete representation distributed constraint optimization message passing message passing solution multi-robot systems network guided multirobot path planning roadmap guided

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Infinite-Horizon Proactive Dynamic DCOPs 16

Infinite-Horizon Proactive Dynamic DCOPs

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16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Hoang, Khoi D. Hou, Ping Fioretto, Ferdinando Yeoh, William Zivan, Roie Yokoo, Makoto New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel Kyushu Univ Dept Informat Fukuoka Japan

ISBN: (纸本)9781510855076

The distributed constraint optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. Researchers have recently extended this model to Proactive Dynamic DCOPs (PD-DCOPs) to capture the inherent dynamism present in many coordination problems. The PD-DCOP formulation is a finite-horizon model that assumes a finite horizon is known a priori. It ignores changes to the problem after the horizon and is thus not guaranteed to find optimal solutions for infinite-horizon problems, which often occur in the real world. Therefore, we (i) propose the Infinite-Horizon PD-DCOP (IPD-DCOP) model, which extends PD-DCOPs to handle infinite horizons;(ii) exploit the convergence properties of Markov chains to determine the optimal solution to the problem after it has converged;(iii) propose three distributed greedy algorithms to solve IPD-DCOPs;(iv) provide theoretical quality guarantees on the new model;and (v) empirically evaluate both proactive and reactive algorithms to determine the tradeoffs between the two classes. The final contribution is important as, thus far, researchers have exclusively evaluated the two classes of algorithms in isolation. As a result, it is difficult to identify the characteristics of problems that they excel in. Our results are the first in this important direction.

关键词： distributed constraint optimization Dynamic DCOPs Stochastic DCOPs

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Graph Based optimization For Multiagent Cooperation 18

Graph Based Optimization For Multiagent Cooperation

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18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Singh, Arambam James Kumar, Akshat Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450363099

We address the problem of solving math programs defined over a graph where nodes represent agents and edges represent interaction among agents. The objective and constraint functions of this program model the task agent team must perform and the domain constraints. In this multiagent setting, no single agent observes the complete objective and all the constraints of the program. Thus, we develop a distributed message-passing approach to solve this optimization problem. We focus on the class of graph structured linear and quadratic programs (LPs/QPs) which can model important multiagent coordination frameworks such as distributed constraint optimization (DCOP). For DCOPs, our framework models functional constraints among agents (e.g. resource, network flow constraints) in a much more tractable fashion than previous approaches. Our iterative approach has several desirable properties-it is guaranteed to find the optimal solution for LPs, converges for general cyclic graphs, and is memory efficient making it suitable for resource limited agents, and has anytime property. Empirically, our approach provides solid empirical results on several standard benchmark problems when compared against previous approaches.

关键词： distributed constraint optimization multiagent cooperation mathematical optimization

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Preference Elicitation for DCOPs 23rd

Preference Elicitation for DCOPs

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33rd International Conference on Logic Programming / 23rd International Conference on Principles and Practice of constraint Programming (CP) / 20th International Conference on Theory and Applications of Satisfiability Testing (SAT)

作者： Tabakhi, Atena M. Le, Tiep Fioretto, Ferdinando Yeoh, William Washington Univ Dept Comp Sci & Engn St Louis MO 63110 USA New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9783319661582;9783319661575

distributed constraint optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem. Motivated by this limitation, we make the following contributions in this paper: (1) We propose a general model for preference elicitation in DCOPs;(2) We propose several heuristics to elicit preferences in DCOPs;and (3) We empirically evaluate the effect of these heuristics on random binary DCOPs as well as SHDS problems.

关键词： distributed constraint optimization Smart homes Preference elicitation

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Towards the decentralized coordination of multiple self-adaptive systems

Towards the decentralized coordination of multiple self-adap...

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4th IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Dragan, Paul-Andrei Metzger, Andreas Pohl, Klaus Univ Duisburg Essen Paluno Ruhr Inst Software Technol Essen Germany

ISBN: (纸本)9798350337440

When multiple self-adaptive systems share an environment and goals, they may coordinate their adaptations to avoid conflicts and satisfy their goals. There are two approaches to coordination. (1) Logically centralized, where a supervisor has complete control over the self-adaptive systems. Such an approach is infeasible when the systems have different owners or administrative domains. (2) Logically decentralized, where coordination is achieved through direct interactions. Because the individual systems have control over the information they share, decentralized coordination accommodates multiple administrative domains. However, existing techniques do not account simultaneously for local concerns, e.g., preferences, and shared concerns, e.g., conflicts, which may lead to goals not being achieved as expected. We address this shortcoming by expressing both types of concerns within one constraint optimization problem. Our technique, CoADAPT, introduces two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms. As a first step in realizing CoADAPT, we focus on the coordination of adaptation planning strategies, traditionally addressed only with centralized techniques. We show the feasibility of CoADAPT in an exemplar from cloud computing and analyze experimentally its scalability.

关键词： self-adaptive systems coordination distributed constraint optimization cloud computing

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Multi-Variable Agents Decomposition for DCOPs to Exploit Multi-Level Parallelism 14

Multi-Variable Agents Decomposition for DCOPs to Exploit Mul...

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14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Fioretto, Ferdinando Yeoh, William Pontelli, Enrico New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA

ISBN: (纸本)9781450334136

Current DCOP algorithms suffer from a major limiting assumption-each agent can handle only a single variable of the problem-which limits their scalability. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition, which: (i) Exploits co-locality of an agent's variables, allowing us to adopt efficient centralized techniques;(ii) Enables the use of hierarchical parallel models, such us those based on GPGPUs;and (iii) Empirically reduces the amount of communication required in several classes of DCOP algorithms. Experimental results show that our MVA decomposition outperforms non-decomposed DCOP algorithms, in terms of network load and scalability.

关键词： distributed constraint optimization DCOP GPGPU

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