一种基于行为的双层动态智能体结构及其在RoboCup中的应用

该文针对目前智能体结构模型的问题,提出了一种基于行为的双层动态智能体结构模型,它是一种混合结构模型,包括有反应式结构和慎思结构,并采用自信度来连接这两种结构,既可以提高在实时动态环境下智能体反应的敏捷性,也使自主机器人能够在动态环境下识别任务。这种混合结构模式已成功地应用于RoboCup仿真机器人足球赛中,并取得了比较好的成绩。     

基于强化学习的多智能体协作方法研究

为了在连续和动态的环境中处理智能体不断变化的需求，我们通过利用强化学习来研究多机器人推箱子问题，得到了一种智能体可以不需要其它智能体任何信息的情况下完成协作任务的方法。强化学习可以应用于合作和非合作场合，对于存在噪声干扰和通讯困难的情况，强化学习具有其它人工智能方法不可比拟的优越性。     

一种基于对手行为预测的多智能体合作研究

在动态对抗性环境下竞争的多智能体系统里，能够对对手行为进行及时而准确的预测，从而采取相应对策，是战胜对手的必要条件。本文提出了一种预测对手行为的新方法，并在此基础上实现了多智能体的合作。最后以RoboCup仿真比赛作为实验平台证明了该方法的可行性和有效性。     

多智能体协作方法及其应用研究

将复杂系统分解成由多个智能体构成的合作多智能体系统，建立了多智能体系统的决策模型，能动态实时地计算每一时刻智能体的决策局势，适应环境的动态变化．采用多智能体方法对半导体生产进行调度，提高了半导体生产线设备的利用率，缩短了单位工件的加工时间．     

多智能体车间调度系统研究

现在制造业所面临的动态需求使得其必须具有更加灵活的应变机制，这使得车间调度问题变得越来越复杂。本文采取多智能体系统技术（MAS）设计了一个包含四个智能体（agent）的多智能体车间调度系统，分别为车间调度智能体、任务分配智能体、车间资源智能体以及拍卖智能体。通过这四个智能体的通信、交互和合作，系统可以给出一个满足当前制造需求的调度最优结果。     

基于进化HMM 模型的动态Agent 系统建模方法

提出一种基于隐式Markov模型（HMM）的进化建模方法．使用进化算法随机搜索HMM的模型空间，自动选择HMM的结构和参数-完成对动态智能体系统行为的建模，学习智能体对周围环境的分割和反映方式．实验结果表明，该方法可以很好地搜索HMM的模型空间，并且避免了人工确定HMM模型结构的困难和手工设计模型所需的多次反复．     

基于多智能体的新型智能决策支持系统体系结构的产生和发展

智能决策支持系统和多智能体是近年来人工智能领域研究的热点。本文从决策支持系统的基本结构出发，针对企业科学决策的要求，介绍了三种基于多智能体的智能决策支持系统的体系结构，以实现分布式群体决策支持，具有控制Agent的群体决策支持以及大型企业的分层动态柔性决策支持。     

基于SEBI系统的多智能体通信机制研究

首先介绍了SEBI多智能体系统模型，并通过对模型中各智能体功能的分析，提出了利用智能体喜好地址表进行通信．通过相互问的接收系数来实现网状链式信息传输的思想．每个智能体是网络中的一个节点，节点可直接与其相连的智能体进行通信，也可通过多个链接方式与其它智能体进行间接通信，从而可避免多智能体的NEXP-完全及集中控制存在的信息阻塞等问题，一定程度地解决了多智能体通信与合作中的分布与集中这一矛盾．本文还通过对智能体问通信机制的分析来探讨SEBI系统中智能体的添加与删除机制及冲突预警机制等．模拟通信实例结果表明，SEBI模型系统可较好地解决多智能体问的NEXP-完全问题及具有冲突预警、层次分明以及安全稳定等优点。     

基于Agent的CGF行为建模技术研究

对于含有智能体、智能行为的复杂系统仿真，传统的建模方法难以表述智能体的精神状态。Agent技术作为面向对象技术的延伸，为解决这类复杂系统的建模与仿真问题提供了有效手段。本文综合论述了基于Agent的CGF行为建模技术，最后并阐述了行为建模研究面临的困难。     

面向网络计算的移动智能体研究与实现

本文围绕移动智能体的以下五个问题进行了探讨 :( 1 )什么是移动智能体与移动智能体系统 ?文中分析了移动智能体的本质特性 ,通过形式化方法认识了移动智能体的核心属性与行为模型 ,给出了移动智能体的计算模型与生命周期模型 ,澄清了移动智能体系统的基本组成。( 2 )为什么要引入移动智能体 ?本文对移动智能体的效用作了深入地探讨 ,认为移动智能体并非一个全新的、替代性的计算模式 ,而是对当前网络计算模式的一个有益补充与归纳。其优势在于提供了一个网络时代的通用计算框架 ,使得基于信息的分布式系统可以简单地、有效地建模与实现 ,智…     

A study of evolution strategy based cooperative behavior in collective agents

The following paper introduces an evolution strategy on the basis of cooperative behaviors in each group of agents. The evolution strategy helps each agent to be self-defendable and self-maintainable. To determine an optimal group behavior strategy under dynamically varying circumstances, agents in same group cooperate with each other. This proposed method use reinforcement learning, enhanced neural network, and artificial life. In the present paper, we apply two different reward models: reward model 1 and reward model 2. Each reward model is designed as considering the reinforcement or constraint of behaviors. In competition environments of agents, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained as subtracting the values. And we propose an enhanced neural network to add learning behavior of an artificial organism-level to artificial life simulation. In future, the system models and results described in this paper will be applied to the framework of healthcare systems that consists of biosensors, healthcare devices, and healthcare system.     

ALAN: adaptive learning for multi-agent navigation

In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and obstacles, often without communication. Existing methods compute motions that are locally optimal but do not account for the aggregated motions of all agents, producing inefficient global behavior especially when agents move in a crowded space. In this work, we develop a method that allows agents to dynamically adapt their behavior to their local conditions. We formulate the multi-agent navigation problem as an action-selection problem and propose an approach, ALAN, that allows agents to compute time-efficient and collision-free motions. ALAN is highly scalable because each agent makes its own decisions on how to move, using a set of velocities optimized for a variety of navigation tasks. Experimental results show that agents using ALAN, in general, reach their destinations faster than using ORCA, a state-of-the-art collision avoidance framework, and two other navigation models.     

Herding cows with JIAC V

In this article we report on the JIAC V team for the agent programming competition 2009, going through the different phases of development and describing the JIAC V agent framework. Based on an iterative approach, we identified and implemented different agent roles. While there is no explicit team concept, the agents cooperate by informing each other of their perceptions and intentions, which leads to emergent team behavior which very dynamically and flexibly reacts to the state of the game.     

任务调度机制中代理的一种反社会性策略

任务调度机制中信誉度模型的设计用来应付代理参与者的自私行为。机制假定代理都是自私的,每个代理的目标就是最大化自身利益。但除了最大化自身利益,有的代理还会设法让其它代理遭受损失,这种代理称作反社会性代理,反社会性代理会尽量争取相对于其他代理尽可能多的利益。描述了代理的一种反社会行为,并提出了一种反社会性策略,反社会性代理可以利用这种策略减少其它代理的利益。     

基本DFL的多Agent协调工作机制研究

多agent的协调性是多agent系统与其它相关研究领域区别开来的关键性问题之一, 也是保证多个智能系统能在一起共同工作的关键.由于agent它自身是一个主体,就其本质上 讲是一个动态的客体,因此动态性和模糊性是agent的主要特性,而多agent之间这个问题体现 的就更为突出.因此,该文基于DFL对多agent协调工作机制进行研究,给出agent的能力表现 模型、能力扩展模型等内容,为人们解决这方面的问题提供理论依据.     

Collective behavior in the Stackelberg model under incomplete information

We present the Stackelberg model with linear demand and cost functions for the agents where the leader agent and follower agents have imprecise initial information regarding the marginal costs of competitors. Agents dynamically refine their perceptions and actions based on observing the actions other agents. We obtain necessary and sufficient conditions of the event that the dynamic converges to a Stackelberg equilibrium with true values of marginal costs. We also clarify the situations when agents cannot come to an equilibrium.     

Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.     

面向网构软件的构件自适应机制研究*

结合软件agent技术,从构件角度出发,通过对传统构件进行包装,加入行为规则驱动和行为推导两大机制,提出了一种能够在运行时实时适应用户需求变化和运行环境变化的自适应构件。在自适应构件的运行和支撑方面,实现了一个可动态定制自适应构件行为规则的工具,改进了现有的构件支撑平台使得能满足自适应构件的运行需求;最后通过一个例子说明了自适应构件的设计、实现、运行和演化等一系列工作流程。初步实践表明,上述机制和整套框架为开发具有自适应性的网构软件提供了有效支持。     

A formal architectural model for logical agent mobility

The process of agent migration is the major difference between logical code mobility of software agents and physical mobility of mobile nodes in ad hoc networks. Without considering agent transfer, it would make little sense to mention the modeling of strong code mobility, which aims to make a migrated agent restarted exactly from the state when it was stopped before migration. From the perspective of system's architecture, this paper proposes a two-layer approach for the formal modeling of logical agent mobility (LAM) using predicate/transition (PrT) nets. We view a mobile agent system as a set of agent spaces and agents could migrate from one space to another. Each agent space is explicitly abstracted to be a component, consisting of an environmental part and an internal connector dynamically binding agents with their environment. We use a system net, agent nets, and a connector net to model the environment, agents, and the connector, respectively. In particular, agent nets are packed up as parts of tokens in system nets, so that agent transfer and location change are naturally captured by transition firing (token game) in Petri nets. Agent nets themselves are active only at specific places and disabled at all the other places in a system net. The semantics of such a two-layer LAM model is defined by transforming it into a PrT net. This facilitates the analysis of several properties about location, state, and connection. In addition, this paper also presents a case study of modeling and analyzing an information retrieval system with mobile agents.     

Multi-agent Mediator architecture for distributed manufacturing

A generic Mediator architecture for distributed task planning and coordination has been developed using multi-agent paradigms. In this approach, agents function autonomously as independent computing processes, and dynamic virtual clusters coordinate the agent's activities and decision making. This coordination involves dynamically created coordination agents and resource agents concurrently. The Mediator architecture contains three levels of these coordination agents: the template mediator, the data-agent manager, and the active mediator. The template mediator is the top-level global coordinator. This agent contains both the templates and the cloning mechanism to create the successively lower-level agents. Task plans are decomposed successively into subtasks, which are allocated to dynamically created agent clusters coordinated through data-agent managers and active mediators. Coordination of agent activity takes place both among the clusters and within each cluster. The system dynamically adapts to evolving manufacturing tasks, with virtual agent clusters being created as needed, and destroyed when their tasks are completed. The mediator architecture and related mechanisms are demonstrated using an intelligent manufacturing scheduling application. Both the machines and the parts involved in this production system are considered as intelligent agents. These agents use a common language protocol based on the Knowledge Query Manipulation Language (KQML). The generic Mediator approach can be used for other distributed organizational systems beyond the intelligent manufacturing application it was originally developed for.