实时环境下Agent决策机制研究

在分析已有Agent模型的基础上,提出了一种新的实时Agent模型。这种模型将Agent的审慎型行为和反应型行为结合在一起,其效率比已有的Agent模型有较大的提高。文中还讨论了实时Agent的决策机制,提出用感知器算法对特征进行分类和任意时间算法进行决策。     

实时环境下Agent决策机制研究

随着实时应用的日益广泛，越来越复杂的技术已经被应用到实时系统中．在分析已有Agent模型的基础上，提出了一种新的实时Agent模型．这种模型将Agent的审慎型行为和反应型行为结合在一起，其效率比已有的Agent模型有较大的提高．还讨论了实时Agent的决策机制，提出用感知器算法对特征进行分类和任意时间算法进行决策．     

一种适用于Internet的实时多Agent系统模型

提出了一种基于Agent to Agent架构的,适用于Internet环境的实时多Agent系统模型。重点阐述了该模型中Agent之间的协作方式、调度策略和任务执行的控制策略,并介绍了采用的实时Agent通信语言和通信机制。     

知识Agent在农业决策系统中的应用研究

针对农业领域多Agent系统的特点,本文利用一种基于知识的Agent模型,该模型以知识为核心,以本体作为内部知识表示方法.在农业领域多Agent系统的设计中,采用基于黑板模型的通信方法,避免了复杂的会话层的通信设计.在知识的决策过程中,将本体与当前成熟的基于规则的推理技术相结合,使农业多Agent系统更加灵活和智能.     

基于多Agent的DMS模型研究

配电管理系统(DMS)是供电企业经营管理的重要系统.本文分析了传统DMS平台运行方式和子系统组成,对DMS进行了基于多Agent的研究.DMS中的信息可分为实时与非实时两类,对此模型分别用反应型Agent和智能型Agent进行控制,并根据DMS的工作流程提出了基于多Agent的DMS模型.     

一种基于Multi-Agent的实时监控系统的设计和实现

基于Multi-Agent技术提出了一种多Agent实时安全支持系统结构模型，包括：故障感知Agent、故障诊断Agent和咨询解释Agent。它们的协同工作用于某超高压变电站安全监控环境、为大型实时监控系统的开发提供了一个成功的实例。     

基于OAA的实时多Agent系统研究

本文通过把实时调度Agent作为一个元Agent集成到OAA(Open Agent Architecture)中,提出了一种基于OAA的实时多Agent系统结构.其中实时调度Agent通过用ICL(Inter-agent Communication Language)描述的接口和过程与OAA中的Facilitator进行通信,从而能够为实时Agent的通信、调度和协调提供实时服务,并且支持嵌入不同的实时调度算法以满足不同的系统需求,改进系统的实时性.     

基于CORBA的分布实时事件管理技术研究

基于事件的通信是许多分布系统进行异步交互的重要途径 .传统的分布事件管理机制既无法解决分布应用的异构性、复杂性和集成性 ,又无法充分支持实时环境 .分布对象计算的主流技术 CORBA是解决前一个难题的最佳手段 ,但标准的 CORBA事件服务又缺乏对实时应用的支持 .为此 ,本文研究了事件通信在分布实时环境中呈现出的新特性 ,分析了 CORBA事件服务的不足 ,提出了基于 CORBA的分布实时事件管理技术 .通过实时化标准的 CORBA事件服务 ,设计实现了 DREAMS系统 ,为 CORBA在实时领域的应用推广作出了有意义的尝试 .     

一种Agent协同规范及其应用研究

介绍了一种设计Agent间协同的方法学——AAF协同规范,以主动实时数据仓库系统为例,运用此规范对数据仓库系统中的Agent协同进行分析和描述,从而构建一个基于Agent的数据仓库系统。     

基于Petri网的实时多智能体系统建模

给出基于Petri网的实时多Agent系统建模方法，它通过Petri网建立由接口模块、目标模块、计划模块、调度模块、知识库模块、环境模块、内部模块和控制模块组成的实时Agent模型，抽象和清晰地描述出实时Agent内部和外部特征。     

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。     

一种混合结构的移动机器人导航控制策略

研究了未知环境下移动机器人实时的导航控制问题.采用分布式系统将反射式行为、反应式行为与慎思规划相结合,设计了移动机器人导航控制策略.根据激光雷达传感器信息设计了基于栅格的实时避障算法和解锁策略.通过慎思规划解决了复杂环境下的局部势能陷阱问题.通过自行研制的移动机器人IMR01的实验验证了导航策略的有效性.     

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.     

A hybrid agent architecture integrating desire,intention and reinforcement learning

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal Difference-Fusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be learned and evaluated without the rigidity of user-defined plans as used in traditional BDI systems. For intention and reinforcement learning to work cooperatively, two strategies are presented for combining the intention module and the reactive learning module for decision making in a real time environment. Our case study based on a minefield navigation domain investigates how the desire and intention modules may cooperatively enhance the capability of a pure reinforcement learner. The empirical results show that the hybrid architecture is able to learn plans efficiently and tap both intentional and reactive action execution to yield a robust performance.     

基于本体的MAS黑板模型研究

探讨了基于本体的多智能代理黑板知识库系统(OMSBS)的体系结构及OMABS中Agent的模型、组织和交互方式。提出了一种描述混合知识类型的本体知识表达模型。本体在OMSBS中作为领域知识智能代理的知识库，是知识共享和智能代理之间通讯的基础，设计了一类调度智能代理以控制知识源智能代理的行为。OMSBS可以表达混合类型知识及其推理过程，具有良好的适应性和扩展性，呈现出开放结构。     

基于双向推理的主体框架

文章提出了一个基于双向推理的主体框架FBRA,它是一个混合型的主体框架,主体既是反应的又是慎思的。它的推理内核是正向推理和反向推理相结合。正向推理用于对环境的反应,包括对其他主体的反应。反向推理基于溯因推理,用于信念修正、规划、多主体协调和多主体通信等。     

CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

For a software information agent, operating on behalf of a human owner and belonging to a community of agents, the choice of communicating or not with another agent becomes a decision to take, since communication generally implies a cost. Since these agents often operate as recommender systems, on the basis of dynamic recognition of their human owners’ behaviour and by generally using hybrid machine learning techniques, three main necessities arise in their design, namely (i) providing the agent with an internal representation of both interests and behaviour of its owner, usually called ontology; (ii) detecting inter-ontology properties that can help an agent to choose the most promising agents to be contacted for knowledge-sharing purposes; (iii) semi-automatically constructing the agent ontology, by simply observing the behaviour of the user supported by the agent, leaving to the user only the task of defining concepts and categories of interest. We present a complete MAS architecture, called connectionist learning and inter-ontology similarities (CILIOS), for supporting agent mutual monitoring, trying to cover all the issues above. CILIOS exploits an ontology model able to represent concepts, concept collections, functions and causal implications among events in a multi-agent environment; moreover, it uses a mechanism capable of inducing logical rules representing agent behaviour in the ontology by means of a connectionist ontology representation, based on neural-symbolic networks, i.e., networks whose input and output nodes are associated with logic variables.     

从宏观和微观论多智能体系统与足球机器人系统体系结构

在半自主型的MAS组织结构的基础上,建立具有Agent、逻辑Agent与物理Agent的异构体系,从微观和宏观两种技术路线分析了多智能体系统 (MAS)的组织结构和Agent的体系结构,提出了一种足球机器人系统的分层知识、分层推理的体系结构模型,可简化推理过程,又增强推理能力,提高足球机器人的应变能力和实时性.     

ON THE ROLE OF BDI MODELING FOR INTEGRATED CONTROL AND COORDINATED BEHAVIOR IN AUTONOMOUS AGENTS

This paper describes an architecture for controlling and coordinating autonomous agents, building on previous work addressing reactive and deliberative control methods. The proposed multilayered hybrid architecture allows a rationally bounded, goal-directed agent to reason predictively about potential conflicts by constructing knowledge level models that explain other agents' observed behaviors and hypothesize their beliefs, desires, and intentions; at the same time, it enables the agent to operate autonomously, to react promptly to changes in its real-time environment, and to coordinate its actions effectively with other agents. A principal aim of this research is to understand the role dzfferent functional capabilities play in constraining an agent5 behavior under varying environmental conditions. To this end, an experimental test bed has been constructed comprising a simulated multi-agent world in which a variety of agent configurations and behaviors have been investigated. A number of experimentalfindings are reported.     

基于多模式交互的多移动机器人分布式合作系统

本文研究合作型多移动机器人系统的分布式控制方法.为了保证多机器人间合作的实 时性和高效率,采用了一种分组策略,并提出了将局部感知和组内通信相集成的多模式机器人 间交互方法.考虑到任务的复杂性和真实环境的非结构化特点,构建了将递阶规划技术与基于 行为的反应式控制相结合的递阶混合式协调结构,并采用有限状态机模型实现了规划层与行为 层的协调机制.合作垃圾清运的实验结果证明了上述方法的有效性.