分布式约束满足问题及其在MAS任务分配中的应用*

针对多智能体系统(MAS)任务分配问题中多个任务与MAS两者的分布式特征,将任务分配问题形式化为分布式约束满足问题(DCSP)进行求解,分别建立了以任务为中心和以agent为中心两种MAS任务分配模型,基于改进的DCSP分布式并行求解算法,提出了基于DCSP的MAS任务分配问题求解框架。该方法适合求解agent间通信有随机延迟以及agent间存在多约束的问题,应用实例的求解表明了其实用性与有效性。     

Cooperative output regulation with application to multi-agent consensus under switching network

In this paper, we consider the cooperative output regulation of linear multi-agent systems under switching network. The problem can be viewed as a generalization of the leader-following consensus problem of multi-agent systems. Due to the limited information exchanges of different subsystems, the problem cannot be solved by the decentralized approach and is not allowed to be solved by the centralized control. By devising a distributed observer network, we can solve the problem by both dynamic state feedback control and dynamic measurement output feedback control. As an application of our main result, we show that a special case of our results leads to the solution of the leader-following consensus problem of linear multi-agent systems.     

多智能体机器人系统分散式通信决策研究

为了减少多智能体机器人系统协调所需通信的数量,提出了一种新的方法.利用有向无环图表示团队的可能联合信度,并基于此以分散式的方式制定通信决策,仅当智能体自身的观察信息显示共享信息将导致期望回报升高时才选择通信.通过维持以及推理团队的可能联合信度将集中式单智能体策略应用于分散式多智能体POM-DP问题.通过实验以及一个详细的实例表明,本文方法能够有效地减少通信资源的使用,同时提高分散执行的性能.     

The capacitated multi-AGV scheduling problem with conflicting products: Model and a decentralized multi-agent approach

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production systems in the context of Industry 4.0. This paper investigates an optimization model and a solution using a decentralized multi-agent approach for a new capacitated multi-AGV scheduling problem with conflicting products (CMASPCP) to take full advantage of AGVs. The novelty of the problem and our model lies in the introduction of AGV capacity constraints and constraints arising from conflicting products, i.e. products that cannot be transported together. As the new I4.0 paradigm tends towards decentralized control, we also present a decentralized multi-agent approach in which AGVs autonomously coordinate to solve the task. The performance of the proposed decentralized approach is compared to a mixed-integer linear programming model on a set of 110 problem instances with different sizes and degrees of complexity. The obtained results show that the proposed decentralized multi-agent approach is effective and competitive in terms of the solution quality and computational time.     

When do agents outperform centralized algorithms?

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: (1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, (2) the implementations are systematically evaluated with varying problem properties, and (3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.     

Dynamic workforce allocation in a constrained flow shop with multi-agent system

The present work addresses the problem of real time workforce scheduling in assembly lines where the number of operators is less to the number of workstations.The problem is faced developing a two-steps procedure made of (i) a centralized scheduling based on a constraint optimization problem (COP) for initial operator scheduling, and (ii) a decentralized algorithm performed by a multiagent system (MAS) to manage workers in case of unforeseen events.In the proposed MAS architecture, Agents represent the operators trying to find local assignments for themselves. The system is validated with a simulation model and implemented with a hardware infrastructure in a real assembly line of electromechanical components. The main original contribution of the paper consists in proving – by means of both validation through a simulation model and test in a real assembly line of electromechanical components – that (1) multi-agent systems could be successfully adopted to solve a workforce scheduling problem, and (2) a combined approach consisting of centralized + distributed approach would provide better results compared with the application of one of the two approaches alone.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.     

A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach

Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic.The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.     

分布式电梯群控系统多目标调度算法

基于多代理系统(MAS)的分布式电梯群控系统将电梯及群控器映射为具有不同功能的代理(agent), 呼梯信号的分派通过各agent协商解决, 使梯群调度算法的计算工作量分散到各agent. 基于拟市场模型, 分布式群控算法主要包括较厢代理(C-agent)报价算法及呼梯信号代理(HC-agent)电梯分派算法等. 设计实现了旨在同时降低平均候梯时间、平均乘梯时间及长候梯率的多目标分布式群控算法DMO. 仿真结果表明, 基于MAS的分布式电梯群控系统是可行的, 所设计的分布式群控算法能够使平均候梯时间、平均乘梯时间及长候梯率同时得到优化.     

一种多智能体系统任务竞争模型及算法研究

致力于解决多智能体系统中的任务分配问题，基于社会生活中的竞争现象提出了一种多智能体竞争模型，同时提出了解决多智能体任务分配的详细算法．文章引入博弈论来研究存在相互外部约束条件下的个体选择问题．为了克服求解纳什均衡点的复杂性，本文采用了一步纳什均衡的方法．仿真结果证明了本模型的合理性和算法的有效性．     

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.     

A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems

In real manufacturing environments, the control of system elements such as automated guided vehicles has some difficulties when planning operations dynamically. Multi agent-based systems, a newly maturing area of distributed artificial intelligence, provide some effective mechanisms for the management of such dynamic operations in manufacturing environments. This paper proposes a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. The proposed multi-agent based approach works under a real-time environment and generates feasible schedules using negotiation/bidding mechanisms between agents. This approach is tested on off-line scheduling problems from the literature. The results show that our approach is capable of generating good schedules in real time comparable with the optimization algorithms and the frequently used dispatching rules.     

含电动汽车的智能配电网优化调度研究综述

常规的经济调度已不能满足可再生新能源和电动汽车随机接入所带来的挑战。为了解决接入配电网电动汽车数量逐渐增多和分布式电源并网问题。本文对含电动汽车的智能配电网优化调度进行了详细的分析。首先简要分析了电动汽车接入对电网造成的影响。其次本文从电动汽车接入电网的类型、电动汽车参与优化调度的目标、优化调度模型以及优化调度建模方法四个研究方面详细分析了电动汽车与智能配电网协调优化调度。从优化调度的结果分析可知,把电动汽车考虑进智能配电网的优化调度中能够有效的降低配电网的运行成本,使得车主的充电费用减少,并且提高了分布式电源的利用率。然后对大数据技术在智能配电网优化中的应用进行了简要的介绍。最后对电动汽车与智能配电网协调优化调度提出了展望。     

Diagnosing resource usage failures in multi-agent systems

In the not-so-far future, autonomous vehicles will be ubiquitous and, consequently, need to be coordinated to avoid traffic jams and car accidents. A failure in one or more autonomous vehicles may break this coordination, resulting in reduced efficiency (due to traffic load) or even bodily harm (due to accidents). The challenge we address in this paper is to identify the root cause of such failures. Identifying the faulty vehicles in such cases is crucial in order to know which vehicles to repair to avoid future failures as well as for determining accountability (e.g., for legal purposes). More generally, this paper discusses multi-agent systems (MAS) in which the agents use a shared pool of resources and they coordinate to avoid resource contention by agreeing on a temporal resource allocation. The problem we address, called the Temporal Multi-Agent Resource Allocation (TMARA) diagnosis problem (TMARA-Diag), is to find the root cause of failures in such MAS that are caused by malfunctioning agents that use resources not allocated to them. As in the autonomous vehicles example, such failures may cause the MAS to perform suboptimally or even fail, potentially causing a chain reaction of failures, and we aim to identify the root cause of such failures, i.e., which agents did not follow the planned resource allocation. We show how to formalize TMARA-Diag as a model-based diagnosis problem and how to compile it to a set of logical constraints that can be compiled to Boolean satisfiability (SAT) and solved efficiently with modern SAT solvers. Importantly, the proposed solution does not require the agents to share their actual plans, only the agreed upon temporal resource allocation and the resources used at the time of failure. Such solutions are key in the development and success of intelligent, large, and security-aware MAS.     

用多Agent系统分配具有启动成本的有限资源

提出了基于多Agent系统分配具有启动成本的有限资源的两种方法：集中式求解方法和分布式求解方法。在分布式求解方法中,给出了分布式的连续双向拍卖协议算法,其Agent采用零智慧增强学习策略。实验结果表明：相对于集中式求解方法,使用分布式求解方法是以降低部分效率为代价的,但是当市场的总需求逐渐接近所有卖者能提供的最大资源数时,市场平均效率呈现逐渐递增的趋势。     

基于JADE的多Agent网络管理系统研究与实现

传统的集中式网络管理在系统的可靠性、伸缩性、灵活性等方面存在诸多不足,分布式网络管理为这些问题提供了一种很好的解决方法。同时,多Agent系统(MAS)在处理分布式问题上具有极其显著的优势,与分布式网络管理的需要高度一致。因此,本文以Jade多Agent平台作为开发环境,研究基于多Agent分布式网络管理系统的可行性,并在Linux系统上实现了配置管理、性能管理等功能。     

Managing RFID events in large-scale distributed RFID infrastructures

As RFID installations become larger and more geographically distributed, their scalability becomes a concern. Currently, most RFID processing occurs in a central location, gathering tag scans and matching them to event-condition-action (ECA) rules. However, as the number of scans and ECA rules grows, the workload quickly outpaces the capacity of a centralized processing server. In this paper, we consider the problem of distributing the RFID processing workload across multiple nodes in the system. We describe the problem, and present an overview of our approach. We then formulate two decision models for distributing the processing across the system. One generates an optimal allocation based on global awareness of the state of the system. This problem is NP\mathcal{NP}-hard and assumes that bandwidth and processing resource availability is known in a central location, which is unrealistic in real scenarios. Thus, we use this model as a theoretical optimal model for comparison purposes. The second model generates a set of local decisions based on locally-available processing and bandwidth information, which takes much less information into account than the global model, but still produces useful results. We describe our system architecture, and present a set of experimental results that demonstrate that (a) the global model, while providing an optimal allocation of processing responsibilities, model does not scale well, requiring hours to solve problems that the localized model can solve in a few tens of seconds; (b) the localized model generates usable solutions, differing from the optimal solution on average by 2.1% for smaller problem sizes and at most 5.8% in the largest problem size compared; and (c) the localized approach can provide runtime performance near that of the global model, within 3-5% of the global model, and up to a 55% improvement in runtime performance over a (uniform) random allocation.     

Securing and authenticating healthcare records through blockchain technology

Abstract

Healthcare data is important in making critical policy decisions, patients care, and medical diagnostics to name a few. Due to the importance and market demand, healthcare data is also vulnerable to cyber attacks. The centralized record keeping systems expose a single node for the attackers to attack. A decentralized system is computationally expensive but has the ability to be revolutionary by keeping the patient at the core and providing security, transparency, privacy, and interoperability of the electronic healthcare data. A blockchain is such an implementation of a distributed and decentralized system using reliable cryptographic algorithms. This paper proposes a secure blockchain based architecture tailored specifically to cater to the needs of e-healthcare systems.     

Opportunistically assisted parking service discovery: Now it helps,now it does not

In this paper, we explore the way the discovery of service can be facilitated or not by utilizing service location information that is opportunistically disseminated primarily by the service consumers themselves. We apply our study to the real-world case of parking service in busy city areas. As the vehicles drive around the area, they opportunistically collect and share with each other information on the location and status of each parking spot they encounter. This opportunistically assisted scenario is compared against one that implements a “blind” non-assisted search and a centralized approach, where the allocation of parking spots is managed by a central server with global knowledge about the parking space availability.Results obtained for both uniformly distributed travel destinations and a single hotspot destination reveal that the relative performance of the three solutions can vary significantly and not always inline with intuition. Under the hotspot scenario, the opportunistic system is consistently outperformed by the centralized system, which yields the minimum times and distances at the expense of more distant parking spot assignments; whereas, for uniformly distributed destinations, the relative performance of all three schemes changes with the vehicle volume, with the centralized approach gradually becoming the worst solution and the opportunistic one emerging as the best scheme. We discuss how each approach modulates the information dissemination process in space and time and resolves the competition for the parking resources. We also outline models providing analytical insights to the behaviour of the centralized approach.     

Distributed problem solving in multiagent systems: a spring net approach

This paper presents a new spring net approach for distributed problem solving in MAS. Distributed artificial intelligence consists of distributed problem solving and multi-agent systems. We extend such specialized DPS and MASs to a general MAS, such that an agent may make a trade-off between selfishness and unselfishness, thus adjusting its own personality and autonomy. This alternative to traditional approaches can deal with a variety of complicated social interactions and autonomous behaviors occurring in multiagent systems.