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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

作者： Christopher Portway Edmund H. Durfee University of Michigan Ann Arbor MI

We extend the MC-DCOP model to problems where each agent controls multiple variables, map a service-oriented computing domain to this MV-MC-DCOP model, and use the solutions as a preprocessing step to an existing inex... 详细信息

ISBN: (纸本)9780982657119

关键词： distributed constraint optimization task scheduling multi-agent coordination

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A distributed constraint optimization approach for coordination under uncertainty 09

A distributed constraint optimization approach for coordinat...

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International Conference on Autonomous Agents and Multiagent Systems

作者： James Atlas University of Delaware

ISBN: (纸本)9780981738178

distributed constraint optimization (DCOP) provides a rich framework for modeling multi-agent coordination problems. We present a mapping to DCOP for large-scale team coordination problems that were used in the DARPA Coordinators program. We develop a new DCOP algorithm that scales to this domain and show that our algorithm out-performs other DCOP algorithms for this domain. Our approach is competitive with other general approaches used in the DARPA Coordinators program.

关键词： distributed constraint optimization multi-agent coordination task scheduling

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New Algorithms for Continuous distributed constraint optimization Problems 20

New Algorithms for Continuous Distributed Constraint Optimiz...

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Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

作者： Khoi D. Hoang William Yeoh Makoto Yokoo Zinovi Rabinovich Washington University in St. Louis St. Louis MO USA Kyushu University Fukuoka Japan Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781450375184

distributed constraint optimization Problems (DCOPs) are a powerful tool to model multi-agent coordination problems that are distributed by nature. The formulation is suitable for problems where variables are discrete and constraint utilities are represented in tabular form. However, many real-world applications have variables that are continuous and tabular forms thus cannot accurately represent constraint utilities. To overcome this limitation, researchers have proposed the Continuous DCOP (C-DCOP) model, which are DCOPs with continuous variables. But existing approaches usually come with some restrictions on the form of constraint utilities and are without quality guarantees. Therefore, in this paper, we (i) propose an exact algorithm to solve a specific subclass of C-DCOPs; (ii) propose an approximation method with quality guarantees to solve general C-DCOPs; (iii) propose additional C-DCOP algorithms that are more scalable; and (iv) empirically show that our algorithms outperform existing state-of-the-art C-DCOP algorithms when given the same communication limitations.

关键词： distributed constraint optimization distributed problem solving

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Latency-Aware Local Search for distributed constraint optimization 21

Latency-Aware Local Search for Distributed Constraint Optimi...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Ben Rachmut Roie Zivan William Yeoh Ben-Gurion University of the Negev Washington University in St. Louis

ISBN: (纸本)9781713832621

Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume messages arrive instantaneously or within a (short) bounded delay. Specifically, distributed local search DCOP algorithms have been designed as synchronous algorithms, performing in an asynchronous environment, i.e., algorithms that perform in synchronous iterations in which each agent exchanges messages with all its neighbors. This is true also for an anytime mechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumptions on instantaneous message arrival are relaxed, the state of the art local search algorithms and mechanism do not apply. In this work, we address this limitation by: (1) Investigating the performance of existing local search DCOP algorithms in the presence of message latency; (2) Proposing an asynchronous monotonic distributed local search DCOP algorithm; and (3) Proposing an asynchronous anytime framework for reporting the best solution explored by non-monotonic asynchronous local search DCOP algorithms. Our empirical results demonstrate that, up to some extent, message delays have a positive effect on distributed local search algorithms due to increased exploration. The asynchronous anytime framework proposed, allows a maximal benefit from such latency based explorative heuristics.

关键词： distributed constraint optimization distributed local search Message latency Constrained optimization SEARCHING ALGORITHM algorithms Asynchronous Messages Distribute state of the art Latency

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Proactive Dynamic distributed constraint optimization 16

Proactive Dynamic Distributed Constraint Optimization

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International Conference on Autonomous Agents and Multiagent Systems

作者： Khoi D. Hoang Ferdinando Fioretto Ping Hou Makoto Yokoo William Yeoh Roie Zivan Department of Computer Science New Mexico State University Department of Informatics Kyushu University Department of Industrial Engineering and Management Ben Gurion University of the Negev

ISBN: (纸本)9781510855083

Current approaches that model dynamism in DCOPs solve a sequence of static problems, reacting to changes in the environment as the agents observe them. Such approaches thus ignore possible predictions on future changes. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) we introduce the PD-DCOP model, which explicitly captures dynamic changes of the DCOP over time; (ii) we discuss the complexity of this new class of DCOPs; and (iii) we develop both exact and approximation algorithms with quality guarantees to solve PD-DCOPs proactively.

关键词： distributed constraint optimization DCOP Dynamic DCOP Constrained optimization formalism Approximation algorithms Distribute dynamic New classes currents electric current Solve

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Proactive distributed Emergency Response With Heterogeneous Tasks Allocation

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INTERNATIONAL JOURNAL OF distributed SENSOR NETWORKS 2025年第1期2025卷

作者： Darko, Justice Park, Hyoshin Rockwell Automat Informat Technol Dept Milwaukee WI 53204 USA Old Dominion Univ Dept Engn Management & Syst Engn Norfolk VA 23529 USA

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring these inherent interdependencies while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops a new proactive framework based on the distributed constraint optimization problem (DCOP) to address the above limitations, overcoming conventional TIM models that cannot accommodate the dependencies in the TIM problem. Furthermore, the optimization objective is formulated to incorporate unmanned aerial vehicles (UAVs). The UAVs' role in TIM includes exploring uncertain traffic conditions, detecting unexpected events, and augmenting information from roadway traffic sensors. Robustness analysis of our model for multiple TIM scenarios shows satisfactory performance using local search exploration heuristics. Overall, our model reports a significant reduction in total incident delay compared to conventional TIM models. With UAV support, we demonstrate a further decrease in the total incident delay ranging between 5% and 45% for the different number of incidents. UAVs' active sensing can shorten response time of emergency vehicles and reduce uncertainties associated with the estimated incident delay impact.

关键词： distributed constraint optimization emergency response incident management look-ahead unmanned aerial vehicles

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Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs

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APPLIED INTELLIGENCE 2021年第3期51卷 1733-1746页

作者： Rashik, Mashrur Rahman, Md Musfiqur Khan, Md Mosaddek Mamun-or-Rashid, Md Long Tran-Thanh Jennings, Nicholas R. Univ Dhaka Dept Comp Sci & Engn Dhaka Bangladesh Univ Southampton Sch Elect & Comp Sci Southampton Hants England Imperial Coll London Dept Comp London England Imperial Coll London Dept Elect & Elect Engn London England

The distributed Pseudo-tree optimization Procedure (DPOP) is a well-known message passing algorithm that provides optimal solutions to distributed constraint optimization Problems (DCOPs) in cooperative multi-agent systems. However, the traditional DCOP formulation does not consider constraints that must be satisfied (hard constraints), rather it concentrates only on constraints that place no restriction on satisfaction (soft constraints). This is a serious shortcoming as many real-world applications involve both types of constraints. Traditional DPOP algorithms are not able to benefit from the existence of hard constraints, where an additional calculation is required to handle such constraints. This results in longer runtimes. Thus scalability remains an issue. Additionally, in the standard DPOP, the agents are arranged as a Depth First Search (DFS) pseudo-tree, but recent work has shown that the construction of pseudo-trees in this way often leads to chain-like communication structures that greatly impair the algorithm's performance. To address these issues, we develop an algorithm that speeds up the DPOP algorithm by reducing the size of the messages exchanged and increases parallelism in the pseudo tree. For this purpose, initially, we improve the path for exchanging messages. Next, we introduce a new form of constraint propagation, which we call cross-edge consistency. Our theoretical evaluation shows that our proposed algorithm is complete and correct. In empirical evaluations, our algorithm achieves a significant reduction in the runtime, ranging from 4% to 96%, compared to the state-of-the-art.

关键词： Multiagent system distributed problem solving distributed constraint optimization DPOP

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Message Passing for distributed QoS-Security Tradeoffs

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COMPUTER JOURNAL 2014年第6期57卷 840-855页

作者： Mostafa, Hala Pal, Partha Hurley, Patrick Raytheon BBN Technol Cambridge MA 02138 USA Air Force Res Lab Rome NY 13441 USA

Information assurance (IA) is a growing concern, since almost every aspect of our lives depends on distributed information systems and the frequency and sophistication of cyber attacks targeting these systems is on the rise. However, IA cannot be considered in isolation, as it also affects the Quality of Service (QoS);with limited resources, the security mechanisms employed for IA (e.g. firewalls, antivirus, encryption) usually adversely affect QoS levels delivered by a system. The system therefore needs to make tradeoffs between IA and QoS. These tradeoffs are complicated by the facts that users' relative preferences over QoS-IA change based on the situation, the preferences of different users conflict and tradeoff decisions made at one node in the distributed system typically affect other nodes as well. We address the problem of distributed computation of tradeoffs among various aspects of QoS and IA in a way that maximizes the satisfaction of all stakeholders. Specifically, we want the nodes in the system to make coordinated decisions as to what local actions to take to optimize QoS/IA levels delivered by the system. Our first contribution is formulating this problem as a distributed constraint optimization problem (DCOP). This entails quantifying various notions involved in tradeoffs to be able to compare options in the course of optimization, as well as encoding the effects of various decisions on the quantities we want to optimize. The DCOPs we obtain have cost functions where multiple local configurations result in the same cost. In addition, the corresponding factor graphs contain many cycles. To deal with these issues, our second contribution is a value propagation (VP) algorithm that helps nodes reach a consistent set of decisions even in cyclic factor graphs with non-unique local optima. We present experimental results comparing the performance of the max-sum algorithm with and without VP against other algorithms when applied to domain-inspired and random

关键词： distributed constraint optimization information assurance value propagation

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Channel allocation algorithms for WLANs using distributed optimization

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2012年第6期66卷 480-490页

作者： Monteiro, Tania L. Pellenz, Marcelo E. Penna, Manoel C. Enembreck, Fabricio Souza, Richard Demo Pujolle, Guy PPGIa PUCPR Curitiba PR Brazil CPGEI UTFPR Curitiba PR Brazil Univ Paris 06 F-75252 Paris 05 France

The performance of a wireless local area network (WLAN) depends on the channel assignments among interfering access points (APs). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if no appropriated spectrum planning is employed. In our study we focus on WLANs scenarios where APs may belong to different administrative domains, which is actually a very common situation in dense urban deployments. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods does not guarantee optimal channel assignment. In this paper, therefore, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new distributed channel assignment algorithm named DCAA-O, which can find the optimal solution to the channel assignment problem for a group of APs. A suboptimal strategy denoted DCAA-S is also investigated, which aims at reducing the number of control messages to be exchanged between APs, while still achieving a suboptimal solution which is very close to the optimal one. The simulation results show that the proposed algorithms are able to outperform the best known techniques both in terms of solution quality and number of exchanged messages. (C) 2011 Elsevier GmbH. All rights reserved.

关键词： Wireless networks Channel assignment distributed constraint optimization ADOPT

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MABP: an optimal resource allocation approach in data center networks

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Science China(Information Sciences) 2014年第10期57卷 230-245页

作者： LI XiaoLing WANG HuaiMin DING Bo LI XiaoYong National Key Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.

关键词： data center network resource allocation workload substrate network optimality distributed constraint optimization

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