面向自动文摘的Agent系统的设计和仿真分析

一、引言建造面向自动文摘的多Agent系统是解决文摘系统适用领域的通用性和文摘质量的矛盾的一种可行的方案。要设计和建造这样的多Agent系统,有两个关键问题:首先是确定在一定负载下,面向各个领域的合适的文摘Agent数目,其次就是选择什么样的协调算法。本文给出了在Internet环境下面向自动文摘     

多Agent系统的协作及强化学习算法研究

研究了多Agent环境下的协作与学习.对多Agent系统中的协作问题提出了协作模型MACM,该模型通过提供灵活协调机制支持多Agent之间的协作及协作过程中的学习.系统中的学习Agent采用分布式强化学习算法.该算法通过映射减少Q值表的存储空间,降低对系统资源的要求,同时能够保证收敛到最优解.     

一种基于正关系的多Agent协调算法

多Agent之间的协调(coordination)与协作(cooperation)已经成为多Agent系统(multiagent system,MAS)中的一个关键问题。这是因为MAS的主要研究目标之一就是使得多Agent的信念、意图、期望、行为达到协调甚至协作。在开放、动态的MAS环境下,具有不同目标的多个Agent必须对其资源的使用以及目标的实现进行协调[1,4]。例如,在出现资源冲突时,若没有很好的协调机制,就有可能出现死锁。而在另一种情况下,当单个Agent无法独立完成目标,需要其它Agent帮助时,则需要协作。本文提出了一种基于正关系的多Agent协调机制和协调算法。在该算法中,通过使用这种协调机制,Agent能委托或接受交互中的子计划,从而形成系统负载均衡和有效降低系统运行开销。     

面向企业战略的ERP自动协同决策一致性系统

首先,定义面向企业战略的ERP自动协同决策概念;其次,提出战略自动协同决策一致性模型,并构造由战略决策个体感知Agent、战略决策协同感知Agent、多库协同管理Agent及数据挖掘者Agent组成的多Agent系统结构;最后,用自主开发的ERP协同决策系统,举例说明自动一致性系统应用。     

一种基于社会承诺的Agent组织模型OMSC

多Agent领域所面临的一个重大的挑战是解决开放异质的多Agent系统中自治Agent间的协调问题。多Agent为了协调它们之间的活动,需要进行交互。社会承诺作为一种通信和交互机制,为自治的多Agent提供了一种协调的途径。然而,仅靠交互难以实现多Agent间的协调。Agent组织作为一种协调模型可以有效地控制多Agent间的交互与合作。论文将社会承诺和Agent组织两种协调机制相结合,提出一种基于社会承诺的Agent组织模型OMSC,分析了Agent如何用社会承诺进行推理以及基于社会承诺的多Agent系统并给出了一个实例,为多Agent间的协调提供了一种新的方法。     

一种动态环境下的多Agent协作模型研究

多Agent系统所处的环境是开放的、动态的。如何在该环境下实现多Agent的协调工作是提高系统性能的关键所在。基于此，本文就动态环境下的多Agent协调和协作机制进行了研究，给出了一种多Agent协作模型。     

多Agent的自动协商

协商是多Agent系统实现协调、协作和冲突消解的关键环节。如何构造有效的协商模型来提高Agent的协商能力，是多Agent系统研究中待解决的问题之一。文章主要讨论了双边多项目协商问题，提出了相应的协商模型、协议和协商算法，具有一定的通用性。     

多级正交Agent结构模型

文章用代数理论对Agent的属性和行为特征进行抽象,寻找一种合适的描述多Agent系统的抽象方法。提出了多级正交Agent结构的概念和框架结构,给出了正交Agent结构的核心模型。提出和研究了正交Agent结构的抽象模型和非正交Agent结构的正交化算法。相对于Ferber、Zambonelli和Lesser等人的工作,这种模型使Agent组织结构清晰,易于实现,有利于研究Agent之间的协调求解能力和Agent组织结构的演化能力。     

可再生能源分布式发电能量管理的复杂性研究

本文主要研究可再生能源分布式发电系统能量的优化与协调问题.基于系统能量管理中优化与协调控制问题的复杂性,提出采用基于Agent的建模与仿真方法进行研究.针对系统要求,以分散式发电系统的实体节点的映射封装Agent,提出了智能混合控制Agent的概念,建立了3层混合Agent的内部结构模型;并建立了以个体层、组织层和社会层构成的3层动态层级MAS体系结构,进一步辅以主导Agent和移动Agent辅助系统优化与决策,最终建立了一种新的面向再生能源的分散式发电系统能量管理的MAS宏观模型.最后应用有色Petri网对系统的优化与协调过程进行了动态模拟,验证了所提出的结构的正确性、合理性和有效性.     

多智能体的角色与结构设计分析

角色是面向Agent软件开发方法研究中的一个重要抽象概念。文中认为角色是连接多Agent系统(MAS)微观模型与宏观模型的桥梁,角色与Agent之间的动态性有利于刻画多Agent系统的结构和行为模型。RoboCup是多Agent系统研究的一个很好的平台。建立一支成功的机器人足球队需要很多领域的知识,合理的模型结构和Agent之间的协调与协作是RoboCup比赛中赢球的关键所在。协调与协作是多Agent系统研究的重要课题。     

基于多Agent技术的自动文摘研究

针对自动文摘研究所面临的问题和现有单机自动文摘系统的性能特点,提出了基于多Ａｇｅｎｔ技术的自动文摘系统的构想,给出了其体系结构,阐述了系统的工作原理。该文摘系统是一个面向Ｉｎｔｅｒｎｅｔ的分布式系统,且具有较好的可靠性和开放性。     

实时容错调度算法的自动模拟系统模型与设计

实时容错技术是实时系统中的关键技术之一,而实时容错调度算法则是实时容错研究领域中需要重点研究的问题.传统的实时容错调度算法的性能模拟是通过手工完成的,如果采用自动模拟方法,可以提高模拟实验的效率和准确性.首先给出一个实时容错调度算法的自动模拟系统模型,该模型定义了自动模拟系统中的关键参数.然后,提出了实时容错调度自动模拟算法.最后,研究了该自动模拟系统的软件结构.文章研究的自动模拟系统具重要的指导意义,可以参考该系统设计其他类型的调度算法的自动模拟系统.该系统的实现可大大节省用于研究实时容错调度算法的时间和费用.     

Decentralized MDPs with sparse interactions

Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limited to specific parts of the state space. In this work, we explore how such local interactions can simplify coordination in multiagent systems. We focus on problems in which the interaction between the agents is sparse and contribute a new decision-theoretic model for decentralized sparse-interaction multiagent systems, Dec-SIMDPs, that explicitly distinguishes the situations in which the agents in the team must coordinate from those in which they can act independently. We relate our new model to other existing models such as MMDPs and Dec-MDPs. We then propose a solution method that takes advantage of the particular structure of Dec-SIMDPs and provide theoretical error bounds on the quality of the obtained solution. Finally, we show a reinforcement learning algorithm in which independent agents learn both individual policies and when and how to coordinate. We illustrate the application of the algorithms throughout the paper in several multiagent navigation scenarios.     

TRAMMAS: A tracing model for multiagent systems

Agent's flexibility and autonomy, as well as their capacity to coordinate and cooperate, are some of the features which make multiagent systems useful to work in dynamic and distributed environments. These key features are directly related to the way in which agents communicate and perceive each other, as well as their environment and surrounding conditions. Traditionally, this has been accomplished by means of message exchange or by using blackboard systems. These traditional methods have the advantages of being easy to implement and well supported by multiagent platforms; however, their main disadvantage is that the amount of social knowledge in the system directly depends on every agent actively informing of what it is doing, thinking, perceiving, etc. There are domains, for example those where social knowledge depends on highly distributed pieces of data provided by many different agents, in which such traditional methods can produce a great deal of overhead, hence reducing the scalability, efficiency and flexibility of the multiagent system. This work proposes the use of event tracing in multiagent systems, as an indirect interaction and coordination mechanism to improve the amount and quality of the information that agents can perceive from both their physical and social environment, in order to fulfill their goals more efficiently. In order to do so, this work presents an abstract model of a tracing system and an architectural design of such model, which can be incorporated to a typical multiagent platform.     

Finite‐time distributed control with time transformation

In this article, we propose distributed control algorithms for first‐ and second‐order multiagent systems for addressing finite‐time control problem with a priori given, user‐defined finite‐time convergence guarantees. The proposed control frameworks are predicated on a recently developed time transformation approach. Specifically, our contribution is twofold: First, a generalized time transformation function is proposed that converts the user‐defined finite‐time interval to a stretched infinite‐time interval, where one can design a distributed control algorithm on this stretched interval and then transform it back to the original finite‐time interval for achieving a given multiagent system objective. Second, for a specific time transformation function, we analytically establish the robustness properties of the resulting finite‐time distributed control algorithms against vanishing and nonvanishing system uncertainties. By contrast to existing finite‐time approaches, it is shown that the proposed algorithms can preserve a priori given, user‐defined finite‐time convergence regardless of the initial conditions of the multiagent system, the graph topology, and without requiring a knowledge of the upper bounds of the considered class of system uncertainties. Illustrative numerical examples are included to further demonstrate the efficacy of the presented results.     

Hybrid consensus protocols: an impulsive dynamical system approach

In this article, we develop a novel hybrid control framework to address fast consensus seeking problems for multiagent dynamical systems. Specifically, we present hybrid distributed controller architectures for multiagent coordination to improve the transient performance of coordination tasks. The proposed controller architectures are predicated on some novel hybrid dynamic compensation structures involving the exchange of information between agents. A unique feature of the proposed framework is that the proposed controller architectures are hybrid and appear to achieve finite-time coordination, and hence significantly improve the transient performance of the closed-loop system. The overall closed-loop dynamics under any of these controller algorithms achieving consensus possesses discontinuous flows since the controller algorithms combine logical switchings with continuous dynamics, leading to impulsive differential equations. Several simulation results are provided to validate the proposed consensus protocols.     

Multiagent simulation subsystem of diagnostic complexes based on device models

Application of the multiagent approach in diagnostic systems based on device behavior models is considered. The architecture of a multiagent diagnostic system, as well as the semantic and spatial methods of the distribution of a device model among the agents, is presented. Working algorithms for a simulation subsystem are given and the efficiency of the multi-agent approach in diagnostic systems based on device behavior models is estimated. The described approach is tested for the semantic distribution of a device model among the agents. Our results confirm the efficiency of applying the multi-agent approach in diagnostic systems based on device behavior models.     

多智能体系统动态协调与分布式控制设计

多智能体系统的主要研究目的在于探索由个体之间的相互作用所产生的群体协调现象的内在机制和原理,而控制或反馈在多智能体协调运动中起着至关重要的作用．本文集中讨论了多智能体协调研究中的几个新兴的基本问题,包括输出调节、集合协调和覆盖．文中着重介绍了分布式估计和内模原理两种多智能体系统分布式输出调节方法及相关的研究进展：关于多智能体系统的目标集合协调,本文从集合聚集和集合优化两方面做了详尽论述：多智能体覆盖有多种分类方式,从覆盖对象的特征出发可将其划分为区域覆盖、边界覆盖和动态目标覆盖3种类型,并对它们的研究背景和最新成果予以介绍．另外文章还对多智能体系统协调控制的理论和应用研究进行了展望．     

Design of coordination strategies in multiagent systems via genetic fuzzy systems

This paper suggests an evolutionary approach to design coordination strategies for multiagent systems. Emphasis is given to auction protocols since they are of utmost importance in many real world applications such as power markets. Power markets are one of the most relevant instances of multiagent systems and finding a profitable bidding strategy is a key issue to preserve system functioning and improve social welfare. Bidding strategies are modeled as fuzzy rule-based systems due to their modeling power, transparency, and ability to naturally handle imprecision in input data, an essential ingredient to a multiagent system act efficiently in practice. Specific genetic operators are suggested in this paper. Evolution of bidding strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of auction mechanisms and their role as a coordination protocol. Simulation experiments with a typical power market using actual thermal plants data show that the evolutionary, genetic-based design approach evolves strategies that enhance agents profitability when compared with the marginal cost-based strategies commonly adopted