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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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Applying Max-sum to asymmetric distributed constraint optimization problems

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2020年第1期34卷 1-29页

作者： Zivan, Roie Parash, Tomer Cohen-Lavi, Liel Naveh, Yarden Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

We study the adjustment and use of the Max-sum algorithm for solving Asymmetric distributed constraint optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better on asymmetric problems. Second, we prove that the convergence properties of Max-sum_ADVP (an algorithm that was previously found to outperform standard Max-sum and Bounded Max-sum) and the quality of the solutions it produces, are dependent on the order between nodes involved in each constraint, i.e., the inner constraint order (ICO). A standard ICO allows to reproduce the properties achieved for symmetric problems. Third, we demonstrate that a non-standard ICO can be used to balance exploration and exploitation. Our results indicate that Max-sum_ADVP with non-standard ICO and Damped Max-sum, when solving asymmetric problems, both outperform other versions of Max-sum, as well as local search algorithms specifically designed for solving ADCOPs.

关键词： distributed constraint optimization Incomplete inference distributed algorithms Max-sum

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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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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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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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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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An improved DPOP algorithm based on breadth first search pseudo-tree for distributed constraint optimization

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APPLIED INTELLIGENCE 2017年第3期47卷 607-623页

作者： Chen, Ziyu He, Zhen He, Chen Chongqing Univ Coll Comp Sci Chongqing Peoples R China

Depth First Search (DFS) pseudo-tree is popularly used as the communication structure in complete algorithms for solving distributed constraint optimization Problems (DCOPs) from multiagent systems. The advantage of a DFS pseudo-tree lies in its parallelism derived from pseudo-tree branches because the nodes in different branches are relatively independent and can compute concurrently. However, the constructed DFS pseudo-trees in experiments often come to be chain-like and greatly impair the performances of solving algorithms. Therefore, we propose a new DPOP algorithm using a Breadth First Search (BFS) pseudo-tree as the communication structure, named BFSDPOP. Compared with a DFS pseudo-tree, a BFS pseudo-tree is more excellent on the parallelism as it has much more branches. Another notable advantage is that the height of a BFS pseudo-tree is much lower than that of a DFS pseudo-tree, which gives rise to the shorter communication paths and less communication time. The method of Cluster Removing is also presented to allocate cross-edge constraints to reduce the size of the largest message in BFSDPOP. In the experiment, BFSDPOP with a BFS pseudo-tree and original DPOP with a DFS pseudo-tree are compared on three types of problems - graph coloring problems, meeting scheduling problems and random DCOPs. The results show that BFSDPOP outperforms original DPOP in most cases, which proves the excellent attributes of BFS pseudo-tree over DFS pseudo-tree.

关键词： Multiagent system distributed constraint optimization Breadth first search pseudo-tree DPOP

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Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids

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APPLIED ENERGY 2017年 206卷 1102-1117页

作者： El-Sharafy, M. Zaki Farag, Hany E. Z. York Univ Dept Elect Engn & Comp Sci 4700 Keele St Toronto ON M3J 1P3 Canada

In this paper, an optimization problem is formulated for the automatic back-feed service restoration in smart distribution grids. The formulated problem relies on the structure of smart distribution grids, clustered into multi-microgrids, capable of operating in both grid-connected and islanded modes of operation. To that end, three types of power transfer between the neighboring microgrids, during the restoration processes are introduced: load transfer, distributed generation (DG) transfer, and combined load DG transfer. The formulated optimization problem takes into account the ability of forming new, not predefined islanded microgrids, in the post-restoration configuration, to maximize service restoration. To obviate the need for a central unit, the optimization problem is reformulated, in this work, as a distributed constraint optimization problem, in which the variables and constraints are distributed among automated agents. To reduce the problem complexity, the restoration problem is decomposed into two sequential and interdependent distributed sub-problems: supply adequacy, and optimal reconfiguration. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search to solve distributed sub-problems in a multi-agent environment. Several case studies have been carried out to evaluate the effectiveness and robustness of the proposed algorithm.

关键词： distributed constraint optimization distributed generation Microgrids Restoration Smart distribution grids

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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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distributed constraint optimization for teams of mobile sensing agents

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2015年第3期29卷 495-536页

作者： Zivan, Roie Yedidsion, Harel Okamoto, Steven Glinton, Robin Sycara, Katia Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

Coordinating a mobile sensor team (MST) to cover targets is a challenging problem in many multiagent applications. Such applications are inherently dynamic due to changes in the environment, technology failures, and incomplete knowledge of the agents. Agents must adaptively respond by changing their locations to continually optimize the coverage of targets. We propose distributed constraint optimization problems (DCOP)_MST, a new model for representing MST problems that is based on DCOP. In DCOP_MST, agents maintain variables for their physical positions, while each target is represented by a constraint that reflects the quality of coverage of that target. In contrast to conventional, static DCOPs, DCOP_MST not only permits dynamism but exploits it by restricting variable domains to nearby locations;consequently, variable domains and constraints change as the agents move through the environment. DCOP_MST confers three major advantages. It directly represents the multiple forms of dynamism inherent in MSTs. It also provides a compact representation that can be solved efficiently with local search algorithms, with information and communication locality based on physical locality as typically occurs in MST applications. Finally, DCOP_MST facilitates organization of the team into multiple sub-teams that can specialize in different roles and coordinate their activity through dynamic events. We demonstrate how a search-and-detection team responsible for finding new targets and a surveillance sub-team tasked with coverage of known targets can effectively work together to improve performance while using the DCOP_MST framework to coordinate. We propose different algorithms to meet the specific needs of each sub-team and several methods for cooperation between sub-teams. For the search-and-detection team, we develop an algorithm based on the DSA that forces intensive exploration for new targets. For the surveillance sub-team, we adapt several incomplete DCOP algorithms, inclu

关键词： Multi-agent systems Dynamic distributed problems distributed constraint optimization

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