支持多Agent通信的扩展BDI逻辑

BDI（信念、期望和意图）逻辑是当前agent理论研究的热点课题之一,相关研究工作较少涉及对agent间通信的描述.文章旨在提出一个能表示和推理多agent间基于言语行为理论的通信行为的扩展BDI逻辑.该逻辑是一个多类、带量词并具有分枝时序结构的一阶多模态逻辑,除了包含标准BDI逻辑的模态和时态算子之外,还引入了一些必要的逻辑算子和构造符,用于刻画agent为实现其意图而具有的规划能力,以及对多agent通信给出正确的语用解释.文章讨论了逻辑的语法和语义,并对言语行为进行了逻辑分类和解释.     

基于关系的两维意向结构

从建构agent角度出发,提出了一个基于关系结构的包括agent意向、信念以及目标等认知状态的框架.在此框架中,实现目标的意向形成了两维序结构,其中一维表示意向间的时序关系,另一维表示意向间的相干关系,在此基础上,研究了信念、意向和目标的相互关系.因为摒弃了传统的用模态算子来刻画agent的意向的方法,所以在构建agent时,可以直接采用意向库以及意向间的时序、相干关系来表示agent的意向,从而缩小了agent理论模型与实际agent结构之间的差异,为agent结构的建立提供了必要的理论基础.     

面向agent软件开发方法比较与应用原则

对面向agent软件开发方法的起源、研究现状和发展趋势进行了综逆和讨论,介绍了部分典型方法,首次提出了agent的强、亚和弱自主性的定义。根据各种方法中对agent交互协议的不同描述方式,定义了显式协议和隐式协议,从四个方面对现有的典型方法做了比较,并分析了本文提出概念的应用原则。由于面向agent软件开发方法的研究仍然处于索阶段,这些概念有助于研究人员理解掌握面向agent软件开发方法,对于该领域的发展具有积极的意义。     

多agent系统中agent的行为决策

1.介绍 agent是指在某一环境下能够持续自主运行,具有社会性、反应性、自发性等特征的计算实体。多agent系统由一组具有一定资源和能力、相对独立且交互合作的agent组成。由于多agent系统提供了更高层次的抽象模型,能够自然、贴切、直观地表示现实世界中的计算实体及其问题求解方     

构建一种多agent并行计算模型

文中旨在找出一种新的构建多agent并行计算模型的方法。通过对组合数学中二分图理论的研究,发现可以利用这个数学思想建立一种新的多agent并行计算模型。文中在对多agent计算本质分析的基础上,基于二分图理论,建立了一种新多agent并行模型,给出了基于BDI模型的多agent并行计算模型的流程算法,最后还给出了这种模型的优越性是能最大可能地减少agent之间的冲突。通过上述工作,可为多agent并行求解问题研究提供理论依据。一方面弥补了现有理论的不足,另一方面也为人们进一步研究多agent的并行计算提供了一种解决方案。     

自主Agent结构AASC及其原型支撑系统AASS

介绍了一个能刻画agent的多种特征,尤其是自主性的自主agent结构AASC。此结构结合了BDI结构和情境演算的优点,既能表示agent的信念、目标、策略等心智成分,又能进行行动推理和规划。为了能够方便用户建造自主agent,基于自主agent结构AASC,开发了AASC的原型支撑系统AASS。介绍了AASS的总体结构,讨论了AASS的主要成分类,说明了如何利用该系统开发agent应用系统。     

构建一种多agent并行计算模型

文中旨在找出一种新的构建多agent并行计算模型的方法。通过对组合数学中二分图理论的研究，发现可以利用这个数学思想建立一种新的多agent并行计算模型。文中在对多agent计算本质分析的基础上，基于二分图理论，建立了一种新多agent并行模型，给出了基于BDI模型的多agent并行计算模型的流程算法，最后还给出了这种模型的优越性是能最大可能地减少agent之间的冲突。通过上述工作，可为多agent并行求解问题研究提供理论依据。一方面弥补了现有理论的不足，另一方面也为人们进一步研究多agent的并行计算提供了一种解决方案。     

面向普适计算的多Agent协同设计系统模型

根据普适计算的特点,提出了一种多agent协同设计系统框架模型.考虑到普适计算环境下移动设备的交互和多通道的信息集成,该模型在已有多agent协同设计系统的框架模型上增加了相应的agent,并且在设计agent的结构中包含了通道识别、上下文生成及多通道信息整合模块,以处理多通道的信息.详细介绍了在该agent模型中基于设计上下文的知识表示、搜索和更新式学习算法,以及协同设计系统中多agent知识共享的方法.该研究对于普适计算在设计领域中的应用有重要的意义.     

一种移动agent结构化迁移机制的设计和实现

移动agent计算模式将成为未来网络计算的主流模式.移动agent的迁移机制是其技术核心之一.该文分析了现有移动agent系统中采用的几种代表性迁移技术,提出了一种新的结构化迁移机制.该机制的主要特点如下:(1) agent的旅行计划和功能体完全分离;(2) 旅行计划本身也具有严格定义的结构;(3) 提供了3种灵活有力的迁移模式.因此,它能有效地控制移动agent的复杂度,有利于agent的复用.在该机制的基础上,设计并实现了移动agent系统Mogent1.0.     

一种基于动态基础设施的移动agent消息传递算法

移动agent技术是一种新型的分布式计算模式,它为基于网络的任务分配合作问题提供了一个很好的解决方案.由于agent在网络间的移动.使得agent之间的通信变得极为复杂,已有的agent通信机制和消息传递算法都是建立在固定网络基础设施上的,不具有良好的可扩展性.针对这个问题,提出了一种基于动态基础设施部署的移动agent消息传递算法,较好地解决了在移动的情况下,agent之间通信的扩展性问题.     

Intentional learning agent architecture

Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. In this paper, an architecture for an intentional learning agent is presented. The architecture is an extension of the BDI architecture in which the learning process is explicitly described as plans. Learning plans are meta-level plans which allow the agent to introspectively monitor its mental states and update other plans at run time. In order to acquire the intricate structure of a plan, a process pattern called manipulative abduction is encoded as a learning plan. This work advances the state of the art by combining the strengths of learning and BDI agent frameworks in a rich language for describing deliberation processes and reactive execution. It enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.     

An intelligent agent with layered architecture for operating systems resource management

Abstract: A software agent is defined as an autonomous software entity that is able to interact with its environment. Such an agent is able to respond to other agents and/or its environment to some degree, and has some sort of control over its internal state and actions. In belief–desire–intention (BDI) theory, an agent's behavior is described in terms of a processing cycle. In this paper, based on BDI theory, the processing cycle is studied with a software feedback mechanism. A software feedback or loop‐back control mechanism can perform functions without direct external intervention. A feedback mechanism can continuously monitor the output of the system under control (the target system), compare the result against preset values (goals of the feedback control) and feed the difference back to adjust the behavior of the target system in a processing cycle. We discuss the modeling and design aspects of an autonomous, adaptive monitoring agent with layered control architecture. The architecture consists of three layers: a scheduling layer, an optimizing layer and a regulating layer. Experimental results show that the monitoring agent developed for an e‐mail server is effective.     

Formalisations of Capabilities for BDI-Agents

Intentional agent systems are increasingly being used in a wide range of complex applications. Capabilities has recently been introduced into some of these systems as a software engineering mechanism to support modularity and reusability while still allowing meta-level reasoning. This paper presents possible formalisations of capabilities within the framework of beliefs, goals and intentions and indicates how capabilities can affect agent reasoning about its intentions. We define a style of agent commitment which we refer to as a self-aware agent which allows an agent to modify its goals and intentions as its capabilities change. We also indicate which aspects of the specification of a BDI interpreter are affected by the introduction of capabilities and give some indications of additional reasoning which could be incorporated into an agent system on the basis of both the theoretical analysis and the existing implementation.     

Preference-based reasoning in BDI agent systems

An important feature of BDI agent systems is number of different ways in which an agent can achieve its goals. The choice of means to achieve the goal in made by the system at run time, depending on contextual information that is not available in advance. In this article, we explore ways that the user of an agent system can specify preferences which can be incorporated into the BDI execution process and used to guide the choices made. For example, a user of a travel system can specify a preferred airline, or a particular kind of accommodation, and the system will use this information to satisfy the goal and preferences, if possible. Preferences are specified in terms of properties of goals and resource usage, and are used to make two types of decisions: (a) select a plan when there is a choice and (b) determine the order in which subgoals of a plan should be pursued when their order is not fixed by design. We have implemented our preference framework in Jadex, and provide detailed case studies within the context of a holiday travel agent application.     

应急响应环境下基于BDI模型的Agent结构研究

基于Agent的应急响应建模仿真具有优越性和现实性,通过分析应急响应体系中Agent主体所应具有的特征——反应性、推理性、互动性和能动性,构建了基于BDI模型的反应+思考+通信的混合型Agent结构,并在NetLogo仿真平台上实现。为今后进一步研究基于Agent的应急管理建模仿真奠定研究基础,同时也为在NetLogo平台下开发基于BDI模型的Agent结构提供范式和标准。     

软件Agent技术及基于FIPA的平台体系结构分析

软件Agent技术提供了一种新的计算模式,基于Agent的系统在解决复杂、开放、分布式问题上所体现出的潜力推动了Agent技术的研究和应用。介绍了Agent的概念、分类、BDI理论模型、体系结构、通信语言、本体论和分布式处理技术。对一个符合FIPA规范的Agent系统参考体系结构进行了分析。     

多主体系统的建模技术研究

为了有利于多主体系统的开发,目前急需一种系统建模技术来对系统复杂性进行行之有效的管理。由于面向对象的建模技术已经趋向成熟,该文在面向对象的建模方法的基础上阐述了一种面向主体的建模方法。该方法针对基于一种特定BDI结构的主体,它包括从内部观点建构信念、目标、计划模型（ 即所谓的BDI模型 ）和从外部观点建构单个主体模型和交互模型。而且,该方法也能容易地进行扩展并应用于其他结构的主体建模。文章对各个模型的建造作出了详细介绍。