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2nd IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Kinoshita, Katsuya Iizuka, Kayo Iizuka, Yasuki Tokai Univ Grad Sch Sci Hiratsuka Kanagawa 25912 Japan Senshu Univ Sch Network & Informat Kawasaki Kanagawa Japan Tokai Univ Dept Math Sci Hiratsuka Kanagawa Japan

ISBN: (纸本)9781479921348;9780769550718

This paper proposes a system that supports effective evacuation from danger using a distributed constraint optimization Algorithm. The use of the algorithm facilitates the assisted optimization of people's evacuation timing, by estimating the location of evacuees. This system enables assistance in terms of evacuation guidance to be given to relieve congestion, by calculating evacuation routes via an adhoc network of evacuees' mobile devices (phones, PCs, etc.), intercommunication function, and location information.

关键词： disaster evacuation distributed constraint optimization problem

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Layered distributed constraint optimization problem for Resource Allocation problem in distributed Sensor Networks

Layered Distributed Constraint Optimization Problem for Reso...

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12th International Conference on Principles of Practice in Multi-Agent Systems (PRIMA 2009)

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

ISBN: (纸本)9783642111600

distributed sensor network is an important research area Of multi-agent systems. We focus On a type of distributed sensor network systems that cooperatively observe multiple targets with multiple autonomous sensors that can control their own view. The problem of allocating. observation resource of the distributed sensor network can be formalized as distributed constraint optimization problems. However, in the previous works, the computation cost to solve the resource allocation problem highly increases with its scale/density. In this work, we divide the problem into two layers of problems, and two layered cooperative solvers are applied to those problems. The result of the experiment shows that our proposed method reduces the number of message cycles.

关键词： distributed constraint optimization problem Multi-agent distributed sensor network

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A Partial Decision Scheme for Local Search Algorithms for distributed constraint optimization problems 16

A Partial Decision Scheme for Local Search Algorithms for Di...

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

作者： Yu, Zhepeng Chen, Ziyu He, Jingyuan Deng, Yancheng Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781510855076

Local search algorithms are widely adopted in solving large-scale distributed constraint optimization problems (DCOPs). However, since each agent always makes its value decision based on the values of its neighbors in local search, those algorithms usually suffer from local premature convergence. More concretely, an agent cannot make a wise decision with poor values of its neighbors since its decision space is bound up with those poor values. In this paper, we propose a Partial Decision Scheme (PDS) to relax the decision space of an agent by ignoring the value of its neighbor which has the bad impact on its local benefits. The PDS comprises two partial decision processes: trigger partial decision and recursive partial decision. The former is iteratively performed by agents who cannot enhance their local benefits unilaterally to break out of potential local optima. The latter is recursively performed by neglected agents to improve global benefits. Besides, the trigger conditions along with a self-adaptive probability are introduced to control the use of PDS. The PDS can be easily applied to any local search algorithm to overcome its local premature convergence with a small additional overhead. In our theoretical analysis, we prove the feasibility and convergence of PDS. Moreover, the experimental results also demonstrate the superiority of the use of PDS in the typical local search algorithms over state-of-the-art local search algorithms.

关键词： Multi-agent System distributed constraint optimization problem Incomplete Algorithm Local Search Algorithm Partial Decision

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VLSs: A Local Search Algorithm for distributed constraint optimization problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第3期36卷

作者： Li, Fukui He, Jingyuan Zhou, Mingliang Fang, Bin Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms.

关键词： Multi-agent system distributed constraint optimization problem local search algorithm incomplete algorithm

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A genetic algorithm based framework for local search algorithms for distributed constraint optimization problems

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2020年第2期34卷 41-41页

作者： Chen, Ziyu Liu, Lizhen He, Jingyuan Yu, Zhepeng Chongqing Univ Coll Comp Sci Chongqing Peoples R China

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problems (DCOPs) where each agent holds a value assignment to its variable and iteratively makes a decision on whether to replace its assignment according to its neighbor states. However, the value assignments of their neighbors confine their search to a small space so that agents in local search algorithms easily fall into local optima. Fortunately, Genetic Algorithms (GAs) can direct a search process to a more promising space and help the search process to break up the confine of local states. Accordingly, we propose a GA-based framework (LSGA) to enhance local search algorithms, where a series of genetic operators are redesigned for agents in distributed scenario to accommodate DCOPs. First, a fitness function is designed to evaluate the assignments for each agent, considering the balance of local benefits and global benefits. Then, a new method is provided to decide crossover positions in terms of agent-communication and topological structure of DCOPs. Besides, a self-adaptive crossover probability and a self-adaptive mutation probability are proposed to control the uses of crossover operator and mutation operator, respectively. And more importantly, the LSGA framework can be easily applied in any local search algorithm. The experimental results demonstrate the superiority of the use of LSGA in the typical search algorithms over state-of-the-art incomplete algorithms.

关键词： Multi-agent system distributed constraint optimization problem Incomplete algorithm Genetic algorithm Local search algorithm

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BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

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ARTIFICIAL INTELLIGENCE REVIEW 2018年第2期50卷 161-199页

作者： Chen, Ziyu He, Chen He, Zhen Chen, Minyou Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Coll Elect Engn Chongqing 400044 Peoples R China

distributed constraint optimization problem (DCOP) is a promising framework for modeling a wide variety of multi-agent coordination problems. Best-First search (BFS) and Depth-First search (DFS) are two main search strategies used for search-based complete DCOP algorithms. Unfortunately, BFS often has to deal with a large number of solution reconstructions whereas DFS is unable to promptly prune sub-optimal branch. However, their weaknesses will be remedied if the two search strategies are combined based on agents' positions in a pseudo-tree. Therefore, a hybrid DCOP algorithm with the combination of BFS and DFS, called BD-ADOPT, is proposed, in which a layering boundary is introduced to divide all agents into BFS-based agents and DFS-based agents. Furthermore, this paper gives a rule to find a suitable layering boundary with a new strategy for the agents near the boundary to realize the seamless joint between BFS and DFS strategies. Detailed experimental results show that BD-ADOPT outperforms some famous search-based complete DCOP algorithms on the benchmark problems.

关键词： Multi-agent systems distributed constraint optimization problem Depth-first search strategy Best-first search strategy BD-ADOPT

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distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks

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ARTIFICIAL INTELLIGENCE 2005年第1-2期161卷 55-87页

作者： Zhang, WX Wang, GD Xing, Z Wittenburg, L Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

This research is motivated by some distributed scheduling problems in distributed sensor networks, in which computational and communication resources are scarce. We first cast these problems as distributed constraint satisfaction problems (DisCSPs) and distributed constraint optimization problems (DisCOPs) and model them as distributed graph coloring. To cope with limited resources and restricted real-time requirement, it is imperative to use distributed algorithms that have low overhead on resource consumption and high-quality anytime performance. To meet these requirements, we study two existing DisCSP algorithms, distributed stochastic search algorithm (DSA) and distributed breakout algorithm (DBA), for solving DisCOPs and the distributed scheduling problems. We experimentally show that DSA has a phase-transition or threshold behavior, in that its solution quality degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. We also consider the completeness and complexity of DBA for distributed graph coloring. We show that DBA is complete on coloring acyclic graphs, coloring an acyclic graph of n nodes in O(n(2)) steps. However, on a cyclic graph, DBA may never terminate. To improve DBA's performance on coloring cyclic graphs, we propose two stochastic variations. Finally, we directly compare DSA and DBA for solving distributed graph coloring and distributed scheduling problems in sensor networks. The results show that DSA is superior to DBA when controlled properly, having better or competitive solution quality and significantly lower communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scheduling problems and other distributed problems of similar properties. (C) 2004 Elsevier B.V. All rights reserved.

关键词： distributed constraint satisfaction problem distributed constraint optimization problem distributed sensor networks distributed stochastic search distributed breakout algorithm breakout algorithm phase transitions distributed scheduling

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Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 19688-19698页

作者： Wang, Wanyuan Zhang, Haipeng Qiao, Tianchi Ma, Jinming Jin, Jiahui Li, Zhibin Wu, Weiwei Jiang, Yichuan Southeast Univ Sch Comp Sci & Engn Minist Educ Nanjing 210018 Peoples R China Southeast Univ Key Lab New Generat Artificial Intelligence Techn Minist Educ Nanjing 210018 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545026 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Southeast Univ Sch Transportat Nanjing 210018 Peoples R China

Traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. The challenge of TSC includes 1) real-time signal decision, 2) the complexity in traffic dynamics, and 3) the network-level coordination. Reinforcement learning (RL) methods can query policies by mapping the traffic state to the signal decision in real-time, however, are inadequate for different traffic flow environment. By observing real traffic information, online planning methods can compute the signal decisions in a responsive manner. Unfortunately, existing online planning methods either require high computation complexity or get stuck in local coordination. Against this background, we propose an explicit multiagent coordination (EMC)-based online planning methods that can satisfy adaptive, real-time and network-level TSC. By multiagent, we model each intersection as an autonomous agent, and the coordination efficiency is modeled by a cost function between neighbor intersections. By network-level coordination, each agent exchanges messages of cost function with its neighbors in a fully decentralized manner. By real-time, the message-passing procedure can interrupt at any time when the real time limit is reached and agents select the optimal signal decisions according to current message. Finally, we test our EMC method in both synthetic and real road network datasets. Experimental results are encouraging: compared to RL and conventional transportation baselines, our EMC method performs reasonably well in terms of adapting to real-time traffic dynamics, minimizing vehicle travel time and scalability to city-scale road networks.

关键词： Traffic signal control multiagent coordination distributed constraint optimization problem message-passing

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Node and Link Allocation in Network Virtualization Based on distributed constraint optimization

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2018年第1期26卷 127-146页

作者： Gularte, Alexander R. Mendizabal, Odorico M. Barbosa, Raquel M. Adamatti, Diana F. Univ Fed Rio Grande Ctr Ciencias Computacionais Ave Italia S-NKm 08 Rio Grande RS Brazil

Virtual Networks (VNs) offer a flexible and economic approach to deploy customer suited networks. However, defining how resources of a physical network are used to support VNs requirements is a NP-hard problem. For this reason, heuristics have been used on mapping of virtual networks. Although heuristics do not ensure the optimal solution, they implement fast solutions and showed satisfactory results. This work presents a modeling of the node and link allocation problem using distributed constraint optimization problem (DCOP) with factor graphs, which is a formalism widely used in real distributed optimization problems. In our approach, we use the max-sum algorithm to solve the DCOP. Correctness criteria for this approach are discussed and verifications are conducted through model checking.

关键词： distributed constraint optimization problem Max-sum algorithm Virtual network embedding Resource allocation Verification Model checking

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A Partial Decision Scheme for Local Search Algorithms for distributed constraint optimization problems 17

A Partial Decision Scheme for Local Search Algorithms for Di...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Zhepeng Yu Ziyu Chen Jingyuan He Yancheng Deng College of Computer Science Chongqing University

ISBN: (纸本)9781510855076

Local search algorithms are widely adopted in solving large-scale distributed constraint optimization problems (DCOPs) However, since each agent always makes its value decision based on the values of its neighbors in local search, those algorithms usually suffer from local premature convergence More concretely, an agent cannot make a wise decision with poor values of its neighbors since its decision space is bound up with those poor values In this paper, we propose a Partial Decision Scheme (PDS) to relax the decision space of an agent by ignoring the value of its neighbor which has the bad impact on its local benefits. The PDS comprises two partial decision processes trigger partial decision and recursive partial decision The former is iteratively performed by agents who cannot enhance their local benefits unilaterally to break out of potential local optima. The latter is recursively performed by neglected agents to improve global benefits Besides, the trigger conditions along with a self-adaptive probability are introduced to control the use of PDS The PDS can be easily applied to any local search algorithm to overcome its local premature convergence with a small additional overhead In our theoretical analysis, we prove the feasibility and convergence of PDS Moreover, the experimental results also demonstrate the superiority of the use of PDS in the typical local search algorithms over state-of-the-art local search algorithms.

关键词： Multi-agent System distributed constraint optimization problem Incomplete Algorithm Local Search Algorithm Partial Decision SEARCHING ALGORITHM Multi-agent systems pendrin Planetary Data System Constrained optimization Agents Neighbor bouse Partial

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