多任务联盟形成中的Agent行为策略研究

Agent联盟是多Agent系统中一种重要的合作方式,联盟形成是其研究的关键问题.本文提出一种串行多任务联盟形成中的Agent行为策略,首先论证了Agent合作求解多任务的过程是一个Markov决策过程,然后基于Q-学习求解单个Agent的最优行为策略.实例表明该策略在面向多任务的领域中可以快速、有效地串行形成多个任务求解联盟.     

电子商务中基于社会交换的谈判

以Agent为中介的电子商务研究忽略了社会关系等社会因素对Agent的谈判的影响.本文在分析价值交换和相应的谈判过程中的社会关系基础上,将基于社会关系的谈判与一种扩展价值交换相联结,提出了一种基于价值交换的谈判的理论框架及其实现机制.     

一种扩展的BDI模型研究

本文介绍了Agent社会本质影响Agent思维状态的概念模型.基于Agent角色和其社会关系,提出了社会思维引导新概念.这个新概念扩展了标准的BDI模型,并且解释了常见的协作、协调、谈判等社会现象对Agent解决问题行为的影响.本文最后给出和解释了四种社会思维引导形态.     

面向生物医学命名实体识别的多Agent元学习框架

生物医学命名实体识别是生物医学数据挖掘的基本任务.文中提出了一种将多Agent系统和元学习方法相结合的多Agent元学习框架,应用于生物医学命名实体识别.基层多个学习Agent分别识别不同类型的生物医学命名实体,并通过相关学习Agent之间的通信来交换有益信息以调节个体Agent的行为提高其学习性能,元层Agent综合决策基层学习Agent的学习结果以获得最终的识别结果.元层Agent和基层学习Agent通过局部特征选择法选择适合不同实体类别的敏感特征集合提高了总体识别性能尤其是小类别识别的性能.文中提出的方法有效改善了传统的单一学习模型和全局特征选择方法不能兼顾各类别命名实体识别性能的不足.实验结果表明,文中提出的全新方法在生物医学命名实体识别上取得了优越的性能,在JNLPBA2004测试语料上获得了77.5%的F测度值.     

基于信任度的合一系统社会任务分配

在多智体社会网络中,传统的任务分配模型一般采用直接面向任务执行者的分配机制.它们不考虑社会网络组织结构对任务分配性能的巨大影响,也很少透彻地研究不可靠社会中的任务分配.针对这些问题,本文开创性地研究了软硬件合一系统的任务分配,即按递阶、分层的思想设计了协作组织模型,并基于此提出了面向社区基于社会协调“软件人”的任务分配模型.模型研究过程中,提出了基于直接信任度和社区声誉的社区信任度评估机制、基于社区信任度和社区物理能力的节点选择机制、基于负载均衡的社区内任务分配机制和基于上下文资源的任务再分配策略.实验结果表明：与常见的直接面向任务执行者和基于资源的任务分配模型相比,所提出的模型具有更优的任务分配性能,且对社会任务环境变化具有更好的鲁棒性;社区内基于负载均衡的分配机制和基于上下文资源的再分配策略也有效提高了分配性能,降低了网络中的通信密度.     

基于知识库和改进型免疫算法求解多任务联盟生成问题

联盟生成是MAS的一个关键问题,主要研究如何在MAS中动态生成面向任务的最优Agent联盟.但现有的研究在连续搜索几个类似任务的求解联盟时,都是重新搜索整个求解空间,而没有很好地利用求解的历史信息,从而影响了求解效率.为此,提出了"任务亲和度"的概念,通过建立历史信息的知识库,引入免疫算法来搜索多任务联盟,并对免疫算法进行了改进,提出自适应提取疫苗的策略,在一定程度上加快了解的进化速度.实验结果证明了算法的有效性.     

面向Agent的Petri网及其应用

为了更好地刻画单个Asent的行为和多Asent间复杂、并行的动态交互,将面向Agent的设计思想与Petri网建模方法相结合,形成了一种面向Agent的Petri网(AOPN)模型.首先给出了面向Agent的Petri网的形式化定义,然后利用Petri网的建模工具,以网上购物背景下买卖双方简单交互为例,对基于面向Agent的Petri网模型的系统建模方法进行了研究和分析,有效地解决了系统结构复杂性的问题.     

飞机地面作业调度MAS开发平台的设计与实现

研究归纳了飞机地面作业调度的工作任务和程序.飞机地面作业调度是典型的NP难问题,针对此问题建立了一种多Agent体系结构和一种新颖的多Agent调度算法DSAFO.利用这种结构和算法,设计并实现了以JADE为基础结构的用户友好的面向任务的飞机地面作业多Agent系统开发平台Agsap.该平台使用用户定义的资源、数据接口和任务流图自动创建以任务流为导向的飞机地面服务多Agent资源调度系统.     

移动Agent可靠位置透明通信方法的研究

在Mobile Agent 的应用中,Agent不是孤立地完成任务,而是需要不断地与其它Agent进行协作和信息交换。而Mobile Agent的特性使它的位置又经常变动。文章论述了一种实现Mobile Agent位置透明通信的方法,主要解决Agent位置追踪问题和Agent迁移时的消息处理。     

基于Cougaar的合作伙伴选择系统

提出了一个合作伙伴选择过程模型和基于Cougaar的合作伙伴选择系统，该系统包括核心企业Agent社区和合作伙伴Agent社区，核心企业Agent社区由管理Agent、任务规划Agent和评价Agent组成。介绍了规划Agent把一个任务分解为多个子任务组成的工作流，决策Agent中模糊变量的创建方法、基于XML的模糊规则表示方法和推理机的工作原理。     

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.     

TCLM-P2P:面向P2P社区的任务协作逻辑模型

P2P网络中广泛存在的“free riding”现象使其在任务协作领域的应用受到了极大制约.为了实现P2P网络环境下的有效任务协作,提出了一种具有激励机制的任务协作逻辑模型.基于Agent理论,首先给出了对等体、半对等体、P2P社区等概念;然后在合同网的框架下提出了面向P2P网络社区的任务协作逻辑模型TCLM-P2P（task collaborative logic model oriented to P2P community）.相对于传统的任务协作模型,在合理的前提假设条件下,模型给出了模型公理和协作规则.该模型通过基于虚拟积分的协作算法实现了具有激励机制的P2P网络中的任务分配与协作.原型系统的实现及仿真实验结果表明TCLM-P2P模型具有可行性和有效性:不仅能够激励自利节点主动参与到任务分配与协作中;同时也能在一定程度上抑制节点的free riding行为,从而保障了P2P系统的有序工作.     

Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents

In this paper, we analyze an Internet agent-based market where non-cooperative agents using behavioral rules negotiate the price of a given product in a bilateral and sequential manner. In this setting, we study the optimal decision-making process of a buying agent that enters the market. Our approach is based on Negotiation Analysis (Raiffa, 1982; Sebenuis, 1992) and we consider that the optimizing buying agent maximizes her discounted expected utility using subjective probabilities. The optimal decision-making process of the buying agent is treated as a stochastic control problem that can be solved by dynamic programming. Three types of behavioral agents are studied, namely conceder agents, boulware agents and imitative agents. A set of simulations is undertaken in order to predict the average outcome in a negotiation process for different parameters of the optimizing buying agent and for the three possible selling agents' behaviors. Finally, we compare the performance of the optimizing agent with that of behavioral buying agents.     

CBB模式中基于动态博弈的智能协商

在属于电子商务领域的消费者购买行为(CBB)模式中,协商是一个重要阶段。该文应用经济学中的不完全信息动态博弈方法,辅以人工智能(AI)领域的Agent技术,解决了CBB模式中的智能协商问题,设计了相应的算法,并通过实验分析了该算法的可行性。     

Factors affecting online group buying intention and satisfaction: A social exchange theory perspective

This study investigates factors that affect consumer continuous use intention toward online group buying and the degree that reciprocity and reputation of social exchange, trust, and vendor creativity affect consumer satisfaction and intention toward online purchasing. Data from 215 valid samples was obtained using an online survey. The research model is assessed using partial least squares (PLS) analysis. The results show that the intention to engage in online group buying is predicted collectively by consumer satisfaction, trust, and seller creativity. Consumer satisfaction with online group buying is predicted primarily by trust, followed by consumer reciprocity. The proposed research model explains 67.7% of variance for satisfaction and 39.7% of variance for intention to engage in online group buying. The results suggest that reciprocity, trust, satisfaction, and seller creativity provide considerable explanatory power for intention to engage in online group buying behavior.     

合作Agent的构造问题

多Agent系统模型及其构造方法学是当前Agent技术应用与发展所面临的一个重要课题。以Agent的属性和应用为基础 ,分析了多Agent系统模型与其构造所涉及的几个主要问题 ,给出了一般的分析方法和解决途径     

Is Online Consumers’ Impulsive Buying Beneficial for E-Commerce Companies? An Empirical Investigation of Online Consumers’ Past Impulsive Buying Behaviors

Previous studies emphasized that e-businesses could increase sales with marketing strategies on shopping web sites that induce impulsive buying behavior, and lacked an empirical approach to problems caused by impulsive buying. This study proposes a research model that deals with online consumers’ impulsive buying behavior and empirically validates it. The results show that impulsive buying could have a strong impact on post e-commerce purchase intention and behavior such as actual behavioral return tendency of goods.     

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     

Pricing with local interactions on agent-based electronic marketplaces

This paper investigates the optimal pricing strategies of a selling agent that is randomly matched with several heterogeneous buying agents whose reservation prices are initially unknown. The seller perceives the behaviors of the buying agents through a logistic distribution with unknown parameters. We study the optimal learning by experimentation model of the logistic distribution. We extend this framework to a dynamic pricing model in which the selling agent is randomly matched with buying agents that are able to communicate their purchase experience to other buying agents. We carry out multi-agent system simulations of this dynamic pricing decision problem and we discuss some properties of the price dynamics one can observe on such marketplaces.