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15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Hoang, Khoi D. Fioretto, Ferdinando Hou, Ping Yokoo, Makoto Yeoh, William Zivan, Roie Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Kyushu Univ Dept Informat Fukuoka Fukuoka Japan Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

ISBN: (纸本)9781450342391

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

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Design of a Demand Responsive Transport service using distributed constraint optimization for airport access 8

Design of a Demand Responsive Transport service using Distri...

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8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Parmaksizoglou, Ilias Alexandros Bombelli, Alessandro Sharpanskykh, Alexei Delft Univ Technol Air Transport & Operat Delft Netherlands

ISBN: (纸本)9781665455305

Accessibility is one of the key performance indicators in the evaluation of a multimodal transport system and, as a result, transport planning has become increasingly more oriented towards it. Demand Responsive Transport (DRT) services have been proposed as a measure for increasing accessibility of a Public Transit (PT) network by servicing users in inaccessible areas. Through multimodal planning and coordination, a DRT service can be integrated within the extended PT network and supply the network optimally. In the context of PT users headed toward airports, an integrated DRT service is proposed for those with extended first-mile connections. This service makes use of taxis to transport users to transit points of a dedicated train line supplying a major European airport. Ride-sharing is considered, while optimal order of service and transit points for modal change are determined. To capture the decentralized nature of matching taxis to users, a multi-agent-based algorithm based on distributed constraint optimization Problems (DCOPs) is developed. Real-time information about routes and fixed schedules of the PT network are extracted via a dedicated routing Application Programming Interface (API). Experiments validate the applicability of the proposed solution by reporting a decrease in users' first-mile travel time that is approximately analogous to the modal share the service captures.

关键词： Public Transit Demand Responsive Transport distributed constraint optimization First-mile Service

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Multiple Sampling and Cooperative Search Strategy on Sampling-Based distributed constraint optimization Method

Multiple Sampling and Cooperative Search Strategy on Samplin...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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

ISBN: (纸本)9781467396189

distributed constraint optimization Problem (DCOP) has been studied as a fundamental problem in multiagent system. distributed Gibbs (DGibbs) is a sampling-based solution method that performs a stochastic search on pseudo-trees of DCOPs. Since DGibbs is a synchronous distributed algorithm, where several agents exclusively perform local search, its message communication costs are relatively large. Moreover, due to the redundant sampling, the improvement of solution quality in the first steps is delayed. In this study, we investigate the effects of concurrent sampling in the same messages. In addition, a cooperative search of neighborhood agents improves the solution quality in the first steps of the search processing.

关键词： multiagent distributed constraint optimization sampling

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Considering Equality on distributed constraint optimization Problem for Resource Supply Network

Considering Equality on Distributed Constraint Optimization ...

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11th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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

ISBN: (纸本)9780769548807

distributed resource allocation is an important application of multiagent systems. In this work, we focus on a resource allocation problem that is motivated from a power supply network that contains distributed sources. In the supply network, resources that are initially distributed among source nodes have to be shared among all nodes. The problem is formalized as a resource constrained distributed constraint optimization problem that is an extended class of distributed constraint optimization problems (DCOPs). In the formalization, cost functions represent preferences of agents on resource use. We specifically consider allocating the cost values to agents as evenly as possible. We present several methods to select optimal assignment in consideration of the equality. The characteristics of the proposed methods are experimentally evaluated. Employing histograms and distinguishing types are effective for reducing the variance while requiring high computational costs. The number of histograms is able to be limited without significant lack of the effects. The minimum-maximum cost value as the main objective reduces the number of iterations and histograms.

关键词： resource allocation distributed constraint optimization multi-agent equality

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Leximin Asymmetric Multiple Objective distributed constraint optimization Problem

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COMPUTATIONAL INTELLIGENCE 2018年第1期34卷 49-84页

作者： Matsui, Toshihiro Matsuo, Hiroshi Silaghi, Marius Hirayama, Katsutoshi Yokoo, Makoto Nagoya Inst Technol Nagoya Aichi Japan Florida Inst Technol Melbourne FL 32901 USA Kobe Univ Kobe Hyogo Japan Kyushu Univ Fukuoka Japan

The distributed constraint optimization Problem (DCOP) lies at the foundations of multiagent cooperation. With DCOPs, the optimization in distributed resource allocation problems is formalized using constraint optimization problems. The solvers for the problem are designed based on decentralized cooperative algorithms that are performed by multiple agents. In a conventional DCOP, a single objective is considered. The Multiple Objective distributed constraint optimization Problem (MODCOP) is an extension of the DCOP framework, where agents cooperatively have to optimize simultaneously multiple objective functions. In the conventional MODCOPs, a few objectives are globally defined and agents cooperate to find the Pareto optimal solution. However, such models do not capture the interests of each agent. On the other hand, in several practical problems, the share of each agent is important. Such shares are modeled as preference values of agents. This class of problems can be defined using the MODCOP on the preferences of agents. In particular, we define optimization problems based on leximin ordering and Asymmetric DCOPs (Leximin AMODCOPs). The leximin defines an ordering among vectors of objective values. In addition, Asymmetric DCOPs capture the preferences of agents. Because the optimization based on the leximin ordering improves the equality among the satisfied preferences of the agents, this class of problems is important. We propose several solution methods for Leximin AMODCOPs generalizing traditional operators into the operators on sorted objective vectors and leximin. The solution methods applied to the Leximin AMODCOPs are based on pseudo trees. Also, the investigated search methods employ the concept of boundaries of the sorted vectors.

关键词： leximin preference multiple objectives distributed constraint optimization multiagent cooperation

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DUCT: An Upper Confidence Bound Approach to distributed constraint optimization Problems

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2017年第5期8卷 69-69页

作者： Ottens, Brammert Dimitrakakis, Christos Faltings, Boi Booking Com Amsterdam Netherlands Chalmers Univ Technol Univ Lille Gothenburg Sweden Harvard Univ Cambridge MA 02138 USA Ecole Polytech Fed Lausanne Lausanne Switzerland

We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorithms for DCOPs. Under general assumptions, we theoretically show that the solution found by DUCT after T steps is approximately T-1-close to the optimal. Experimentally, we show that DUCT matches the optimal solution found by the well-known DPOP and O-DPOP algorithms on moderate-size problems, while always requiring less agent communication. For larger problems, where DPOP fails, we show that DUCT produces significantly better solutions than local, incomplete algorithms. Overall, we believe that DUCT is a practical, scalable algorithm for complex DCOPs.

关键词： distributed constraint optimization coordination multiagent systems tree search

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Global constraints in distributed constraint Satisfaction and optimization

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COMPUTER JOURNAL 2014年第6期57卷 906-923页

作者： Bessiere, Christian Brito, Ismel Gutierrez, Patricia Meseguer, Pedro Univ Montpellier CNRS LIRMM Montpellier France Univ Autonoma Barcelona CSIC IIIA E-08193 Barcelona Spain

Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction problem (DisCSP) and distributed constraint optimization problem (DCOP). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global constraints with local consistency (both in the hard and soft cases), in particular, for generalized arc consistency (GAC). We provide experimental evidence of the benefits of global constraints on several benchmarks, both for distributed constraint satisfaction and for distributed constraint optimization.

关键词： distributed constraint satisfaction distributed constraint optimization global constraints soft global constraints

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Proactive distributed constraint optimization Problems 19

Proactive Distributed Constraint Optimization Problems

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Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

作者： Khoi Hoang Washington University in St. Louis St. Louis MO USA

ISBN: (纸本)9781450363099

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 (finite-horizon) Proactive Dynamic DCOPs (PD-DCOPs) and Infinite-Horizon PD-DCOPs (IPD-DCOPs) to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs and IPD-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 these formalisms allows them to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models.

关键词： distributed constraint optimization dcop distributed problem solving

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The multi variable multi constrained distributed constraint optimization framework 10

The multi variable multi constrained distributed constraint ...

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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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Asynchronous algorithms for approximate distributed constraint optimization with quality bounds 10

Asynchronous algorithms for approximate distributed constrai...

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

作者： Christopher Kiekintveld Zhengyu Yin Atul Kumar Milind Tambe University of Southern California

ISBN: (纸本)9780982657119

distributed constraint optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NP-hard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds on solution quality is k-size optimality, which defines a local optimality criterion based on the size of the group of deviating agents. Unfortunately, the lack of a general-purpose algorithm and the commitment to forming groups based solely on group size has limited the use of k-size *** paper introduces t-distance optimality which departs from k-size optimality by using graph distance as an alternative criteria for selecting groups of deviating agents. This throws open a new research direction into the tradeoffs between different group selection and coordination mechanisms for incomplete DCOP algorithms. We derive theoretical quality bounds for t-distance optimality that improve known bounds for k-size optimality. In addition, we develop a new efficient asynchronous local search algorithm for finding both k-size and t-distance optimal solutions --- allowing these concepts to be deployed in real applications. Indeed, empirical results show that this algorithm significantly outperforms the only existing algorithm for finding general k-size optimal solutions, which is also synchronous. Finally, we compare the algorithmic performance of k-size and t-distance optimality using this algorithm. We find that t-distance consistently converges to higher-quality solutions in the long run, but results are mixed on convergence speed; we identify cases where k-size and t-distance converge faster.

关键词： distributed constraint optimization DALO bounds asynchronous algorithms t-distance optimality approximate DCOP incomplete experimentation k-optimality

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