流域管理Multi-Agents系统演进机理分析

针对流域管理中流域机构与行政区划等各相关利益主体在决策过程中具有有限理性、自主性、动态性等特点,将流域管理多Agent系统作为一类复杂适应系统,运用演化博弈理论,建立流域管理中多Agent系统的演化博弈均衡模型,分析多Agent系统中各利益主体之间的竞争与合作机制的动态演变过程。研究结果表明,流域管理多Agent系统的演化方向与博弈双方的支付矩阵以及系统的初始状态相关。通过增加博弈双方群体采取完全合作策略的相对支付和降低从不合作到合作的成本,会有效促进系统由不合作策略向完全合作策略的方向演化。     

一种带概率与效用算子的Agent形式化模型

形式化方法描述Agent时需要考虑信念的不确定性与决策的效用性要素.在经典Agent的BDI形式化模型基础上,定义了Agent形式化语言,引入概率算子与效用算子,提出了Agent形式化模型,在此模型中利用概率算子与效用算子对Agent的信念、愿望、意图与规划等意识属性进行了定义.该模型能满足Agent对逻辑理性、信念的不确定性与决策理性的要求.     

Agent逻辑和真假子集语义

理性Agent规约的形式框架通常基于信念、愿望和意图逻辑.为了克服现有的信念、愿望和意图逻辑中存在的问题,为非正规模态算子提供一种合适的语义表示.讨论了理性Agent性态的抽象规约中对语义表示的要求以及现有的信念、愿望和意图逻辑中存在的问题.介绍了作者开发的真假子集语义及其在Agent形式化中的应用.他们的框架使意图的有问题的性质无效.并且证明通过对模型的代数结构施加一定的约束,能获得许多希望的性质.最后对真假子集语义进行了分析.这一切表明真假子集语义为非正规模态算子提供了一种合适的语义表示,是对经典的正规模态算子可能世界语义的一个重要发展,是理性Agent性态的逻辑规约的有力工具,可应用于建立新的合适的Agent逻辑系统.     

Agent社会理性的研究

MAS(multi-agent system)是由多个自治Agent组成的协商,是合作的Agent社会.在社会背景下,Agent社会理性决定着MAS目标的实现.Jennings虽然给出了Agent社会理性的定义和相应的模型,但没有给出模型参数的具体计算方法及其物理意义.基于协同学原理,指出模型的物理意义是系统的序参量,并将参数的计算分为4步:近似线性化、确定序参量、使用支配原理建立微分方程和解微分方程.     

Agent Oriented Programming进展

1 引言近十年Agent和多Agent系统(MAS)的研究逐渐成为AI学科的热点之一。MAS中Agent是具有思维状态和交互能力的自治实体,彼此通过合作求解复杂问题,适合于动态开放环境。Agent的思维状态通常采用BDI模型,B(信念)、D(愿望)和I(意图)分别用模态算子给出,并在可能世界框架下给出其语义解释。这一模型语义明确直观,但实质上是一个计算资源无限的理想模型,因而在实际系统中,都采用限制和变通的方法来实现Agent,因此导致了所谓的理论脱离实践     

多Agent合作逻辑中的动作与意图

改进并发博弈结构,给出了一个新模型.消除了不同Agent不准执行相同动作这个与常识不符的假定.给出了5个动作相关函数,使得对Agent、动作与状态三者之间的关系在社会法律约束下的深入考察成为可能.在语法层面同时表述动作和社会法律,提高了多Agent合作逻辑的灵活性和表达能力.在多Agent合作逻辑中引入信念算子和意图算子;考察了两种个体意图和两种群体意图;给出了对命题的个体意图的多子集语义,并把它拓展到对命题的群体意图的语义.     

集体理性约束的Agent协作强化学习

将多Agent协作学习过程看作是一个个的阶段博弈,针对博弈中存在多个均衡解的问题,提出一种集体理性约束下的多Agent协作强化学习算法。该算法使得系统中的每个Agent均按照集体利益最大化的集体理性原则进行行为选择,从而解决均衡解一致问题,同时使得集体长期回报值最大化,加快了学习速度。在集体理性的基础上通过评价各Agent对整体任务求解的贡献度,解决信度分配问题。追捕问题的仿真实验结果验证了算法的有效性。     

Agent-BDI逻辑

阐述了Agent的形式化描述应该采用含有正规和非正规模态算子的混合模态逻辑为逻辑工具 的观点.建立了Agent-BDI逻辑的代表系统A-BI,讨论了它的语法和语义.特别是给出了非正 规模态算子基于Kripke标准可能世界的新的语义解释,证明了A-BI逻辑系统不但是可靠的, 而且是完备的.A-BI逻辑系统恰当地刻画了信念与意图的本质与内在联系,可作为Agent形式 化研究的逻辑工具.     

一个松散耦合的多Agent系统通信框架

传统的Agent通信采用紧耦合方式,不利于多Agent系统的扩展和异质Agent之间的互操作.分析了Web服务和语义Web服务的基本模型,借鉴了语义Web服务的体系结构思想和实现手段,在此基础上给出了一个松散耦合的多Agent通信框架.框架强调慎思式多Agent之间合作的起点是Agent对自身能力的描述和发布,通过语法封装和语义映射解决使用不同ACL(Agent通信语言)的Agent交互问题,具有灵活性、可扩展性、简单性和通用性.     

一种结合环境状态的Agent语义模型

在Agent模型的研究中，大部分工作集中在基于Agent的思维状态（BDI）的模型，没有考虑外部环境的影响，或者仅仅将外部环境和内部思维状态笼统混在一起进行分析，没有考虑到它们之间的内在联系，将Agent的BDI思维属性模型与外部环境状态相结合，给出了在部分可观察环境下，结合外部环境状态的MAS语言语法，语义模型，考虑了Agent的可见算子，观察算子和信念算子之间的关系，并通过机器人足球赛的例子，对该语义系统加以具体描述，这些研究推广了Kaelbling,Wooldridge等人的工作。     

An angle-based interest model for text recommendation

Building an interest model is the key to realize personalized text recommendation. Previous interest models neglect the fact that a user may have multiple angles of interest. Different angles of interest provide different requests and criteria for text recommendation. This paper proposes an interest model that consists of two kinds of angles: persistence and pattern, which can be combined to form complex angles. The model uses a new method to represent the long-term interest and the short-term interest, and distinguishes the interest in object and the interest in the link structure of objects. Experiments with news-scale text data show that the interest in object and the interest in link structure have real requirements, and it is effective to recommend texts according to the angles.     

Rational Communication in Multi-Agent Environments

We address the issue of rational communicative behavior among autonomous self-interested agents that have to make decisions as to what to communicate, to whom, and how. Following decision theory, we postulate that a rational speaker should design a speech act so as to optimize the benefit it obtains as the result of the interaction. We quantify the gain in the quality of interaction in terms of the expected utility, and we present a framework that allows an agent to compute the expected utilities of various communicative actions. Our framework uses the Recursive Modeling Method as the specialized representation used for decision-making in a multi-agent environment. This representation includes information about the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents' beliefs, and so on. Decision-theoretic pragmatics of a communicative act can be then defined as the transformation the act induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of interaction, expressed in terms of the expected utilities of the agent's best actions before and after the communicative act. We analyze decision-theoretic pragmatics of a number of important kinds of communicative acts and investigate their expected utilities using examples. Finally, we report on the agreement between our method of message selection and messages that human subjects choose in various circumstances, and show an implementation and experimental validation of our framework in a simulated multi-agent environment.     

兴趣度--关联规则的又一个阈值

关联规则的采掘是数据采掘研究的一个重要方面,分析现有的关联规则采掘算法中所存在的问题：首先是关联规则在其表达形式上没有考虑各种可能的反面示例的影响,因而导致知识表达功能的不够完善;其次是有可能一条规则即使可信度和支持度都很高,仍没有实际意义,甚至是误导性的,因此对关联规则的形式定义作了修改,将运用差异思想引兴起度阈值运用到关联规则中来,并给出其形式定义,在分析了兴趣度的实际意义以后,讨论了举度与概     

The Impact of Short-Sale Constraints on Asset Allocation Strategies via the Backward Markov Chain Approximation Method

This paper considers an asset allocation strategy over a finite period under investment uncertainty and short-sale constraints as a continuous-time stochastic control problem. Investment uncertainty is characterised by a stochastic interest rate and inflation risk. If there are no short-sale constraints, the optimal asset allocation strategy can be obtained analytically. We consider several kinds of short-sale constraints and employ the backward Markov chain approximation method to explore the impact of short-sale constraints on asset allocation decisions. Our results show that the short-sale constraints do indeed have a significant impact on these decisions.     

智能网站Agents的研究

研究智能网站Agents的学习行为 ,提出Agents学习用户获取信息的算法和过程 ,掌握该用户的信息获取倾向 ,建立相应的用户兴趣知识库 ,根据知识库有选择地向用户推荐信息 ,从而实现信息发布和获取的最优化运作     

一个基于XML和多Agent系统的远程教学模型

针对目前远程教学中所存在的一些不足,将多Agent系统和XML技术引入到远程教学中,从而建立了一个含有四类Agent、八种Web服务的远程教学模型XA_DTS。通过该模型,远程教学能够实现有针对性的个性化教学,同时能够激发学生的主观能动性,促进学生的主动学习。     

煤矿井下避灾模型研究

为了研究井下矿工面对灾害时的避灾情况,利用基于Agent的建模仿真方法在RePast仿真平台上建立了煤矿井下避灾模型,并根据井下矿工的实际情况抽象出了Agent的种类.通过对井下矿工面对灾害时的行为模式进行分析,提取出了Agent在面对灾害时的行为决策.通过将Agent的行为决策进行量化,在仿真平台上对矿工的避灾路线实现了仿真.仿真结果表明：该模型能够实时动态地显示矿工的避灾情况,灾害发生时,利用该模型生成的避灾路线能够提高Agent避灾成功的比例.     

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Automatic detection of a user's interest in spoken dialog plays an important role in many applications, such as tutoring systems and customer service systems. In this study, we propose a decision-level fusion approach using acoustic and lexical information to accurately sense a user's interest at the utterance level. Our system consists of three parts: acoustic/prosodic model, lexical model, and a model that combines their decisions for the final output. We use two different regression algorithms to complement each other for the acoustic model. For lexical information, in addition to the bag-of-words model, we propose new features including a level-of-interest value for each word, length information using the number of words, estimated speaking rate, silence in the utterance, and similarity with other utterances. We also investigate the effectiveness of using more automatic speech recognition (ASR) hypotheses (n-best lists) to extract lexical features. The outputs from the acoustic and lexical models are combined at the decision level. Our experiments show that combining acoustic evidence with lexical information improves level-of-interest detection performance, even when lexical features are extracted from ASR output with high word error rate.     

A framework for meta-level control in multi-agent systems

Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions. This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies.