基于Nearest-Biclusters协作过滤技术的效用图结构学习算法

在多议题协商研究中,议题之间的依赖关系增加了协商Agent效用函数的复杂性,从而使得多议题协商变得更加困难.基于效用图的多议题依赖协商模型是体现议题间依赖关系的多议题协商模型.在该协商模型中,协商双方仅需要较少的协商步数就能够找到满足Pareto效率的协商结局.如何有效地学习买方Agent的效用图结构是该协商模型的关键.文中基于Nearest-Biclusters协作过滤技术的思想提出了一种新的效用图结构学习算法(NBCFL算法).该算法首先利用Nearest-Biclusters协作过滤技术发现买方偏好的局部匹配特性,提取与当前买方Agent类型相同的买方Agent所产生的协商历史记录,然后通过计算各议题间的依赖度学习买方Agent的效用图结构.实验表明在参与协商的买方Agent类型不同的条件下,NBCFL算法比IBCFL算法能更好地学习买方Agent的效用图结构.     

基于偏好的双边多议题协商优化

提出一种优化的自动协商模型。Agent在信知不完全的情况下通过学习交互历史和在线协商信息获取对手的偏好,结合贝叶斯方法和支持向量机学习对手偏好,基于保留值和权重提出一种决策模型。通过实验比较和分析,该模型能有效降低协商次数,提高协商双方的联合效用。在信息保密和先验知识未知的环境下,该模型仍然表现出了较高的效用和效率。     

协商Agent的历史学习算法研究

文章以买方Agent的观点对交易平台上获得的对方Agent历史协商信息进行分析,并根据其特点做初步过滤。在此基础上,该文针对现有协商模型中存在的问题,提出了一个Agent协商历史学习算法,并实验说明了其可行性。该算法可用于Agent协商前初始信念的创建,对Agent在协商中策略的选择、执行具有指导作用。     

基于对手案例学习的多议题协商决策模型

为了解决多Agent系统(MAS)协商双方在信息对称情况下的自动协商问题,提出了一种用基于支持向量机算法的间接学习对手协商态度的协商方法,提出了不完全信息条件下基于案例和对策论的Agent多议题Pareto最优协商模型,通过支持向量机的方法来学习协商轨迹,得到协商对手在每个协商项的态度,然后利用学习得到的对手协商态度,构造了一个协商的决策模型,此模型能同时基于对手的态度和自身的偏好来做出协商决策。最后通过实验验证了该方法的先进性。     

多Agent协商竞争模型的研究及仿真

在利用多Agent构建人工系统的过程中,Agent的利益偏好不同及资源有限等因素会引起冲突,而多Agent之间有效的协商竞争能够减少冲突,提高整体性能。为此,提出一种新的多Agent协商竞争模型,该模型依托协商信息,提高竞争对手策略预测准确率,同时采用概率最大化利益行为选择策略,从而缩短协商竞争时间。     

一种劝说式多Agent多议题协商方法

多Agent系统中的协商问题往往由许多议题组成，导致问题空间十分庞大．传统的协商方法通过对问题空间进行穷尽搜索来找到最优解，并不适合多议题协商．而且，传统的方法不考虑协商偏好变化的情况，使得Agent在不完全及不正确环境下找到的最优解并不合理．提出一种劝说式多Agent多议题协商方法．借助信念修正这一有效的推理工具，协商Agent能够在协商过程中接受协商对手的劝说，考虑对手对协商议题的偏好，并根据一种基于辩论的信念修正方法调整自身的偏好．这样就能够使协商Agent对变化的协商环境具备适应性，从而提高协商的效率及正确率，快速准确地达成协议．     

多Agent自动协商的公平性研究

为了能够快速、高效地进行Agent协商,构建一个优化的多Agent协商模型。在这个模型的基础上,提出了一个基于协商各方公平性的协商学习算法。算法采用基于满意度的思想评估协商对手的提议,根据对方Agent协商历史及本次协商交互信息,通过在线学习机制预测对方Agent协商策略,动态得出协商妥协度并向对方提出还价提议。最后,通过买卖协商仿真实验验证了该算法的收敛性,表明基于该算法的模型工作的高效性、公平性。     

一种基于Agent技术的安全能力自动协商机制

适应多安全域环境的安全能力自动协商机制包括了一种基于Agent的安全能力协商模型、适用于该模型的协商Agent和协商流程。该机制中，通过定义安全能力协商描述语言(SanDL)文档，用户可以方便准确地将协商要求和协商策略告知协商Agent，由协商Agent自动完成协商过程。在协商Agent中通过定义通信适配器和一套基本协商原语，使协商Agent的通信接口更加安全、灵活、易于扩展。     

基于遗传算法的Agent多边多议题协商模型

提出了一种基于遗传算法的多边多议题协商模型,该模型能够根据协商过程中对手提议的变化情况动态估计对手偏好,提高了适应度函数的判断能力,促使协商达成一致.实验分析表明,该模型使各Agent均能在短时间内协商成功,并且各Agent获得的效用总和近似于最高社会效益,从而验证了该模型的有效性.     

一个基于Agent的轿车市场自动协商模型

该文提出了一个针对轿车市场中交易协商的双边多议题自动协商模型，该模型具有如下特点：用基于效用的相似度比较法实现Agent智能搜索；模型采用学习机制包括历史学习和Q-学习，历史学习机制用于Agent协商前初始信念的创建，对Agent在协商中策略的选择、执行具有指导作用。Q-学习机制用于生成协商提议，使得Agent能够在半竞争、信息不完全和不确定以及存在最大协商时间的情况下，更为有效地完成多议题协商。     

Automated Negotiation from Declarative Contract Descriptions

Our approach for automating the negotiation of business contracts proceeds in three broad steps. First, determine the structure of the negotiation process by applying general knowledge about auctions and domain–specific knowledge about the contract subject along with preferences from potential buyers and sellers. Second, translate the determined negotiation structure into an operational specification for an auction platform. Third, after the negotiation has completed, map the negotiation results to a final contract. We have implemented a prototype which supports these steps by employing a declarative specification (in courteous logic programs) of (1) high–level knowledge about alternative negotiation structures, (2) general–case rules about auction parameters, (3) rules to map the auction parameters to a specific auction platform, and (4) special–case rules for subject domains. We demonstrate the flexibility of this approach by automatically generating several alternative negotiation structures for the domain of travel shopping in a trading agent competition.     

A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation

This paper describes a negotiation model that incorporates real-time issues for autonomous agents. This model consists of two important ideas: a real-time logical negotiation protocol and a case-based negotiation model. The protocol integrates a real-time Belief-Desire-Intention (BDI) model, a temporal logic model, and communicative acts for negotiation. This protocol explicitly defines the logical and temporal relationships of different knowledge states, facilitating real-time designs such as multi-threaded processing, state profiling and updating, and a set of real-time enabling functional predicates in our implementation. To further support the protocol, we use a case-based reasoning model for negotiation strategy selection. An agent learns from its past experience by deriving a negotiation strategy from the most similar and useful case to its current situation. Guided by the strategy, the agent negotiates with its partners using an argumentation-based negotiation protocol. The model is time and situation aware such that each agent changes its negotiation behavior according to the progress and status of the ongoing negotiation and its current agent profile. We apply the negotiation model to a resource allocation problem and obtain promising results.     

Exploring agent support at the user interface in e-commerce applications

In this paper, we present some of the results from our ongoing research work in the area of ‘agent support’ for electronic commerce, particularly at the user interface level. Our goal is to provide intelligent agents to assist both the consumers and the vendors in an electronic shopping environment. Users with a wide variety of different needs are expected to use the electronic shopping application and their expectations about the interface could vary a lot. Traditional studies of user interface technology have shown the existence of a ‘gap’ between what the user interface actually lets the users do and the users’ expectations. Agent technology, in the form of personalized user interface agents, can help to narrow this gap. Such agents can be used to give a personalized service to the user by knowing the user’s preferences. By doing so, they can assist in the various stages of the users’ shopping process, provide tailored product recommendations by filtering information on behalf of their users and reduce the information overload. From a vendor’s perspective, a software sales agent could be used for price negotiation with the consumer. Such agents would give the flexibility offered by negotiation without the burden of having to provide human presence to an online store to handle such negotiations. Published online: 25 July 2001     

多Agent谈判：一种基于模糊逻辑控制的方法

在分析各种Agent谈判模型不足的基础上，首先给出基于黑板模式的谈判Agent体系结构，然后设计了一个通用的谈判Agent建议的综合评价方法。此外，应用标准可加性模型原理设计了谈判Agent的策略模型，从而构建了应用范围广泛的自动化谈判Agent模型。     

个性化电子商务购物模型研究

电子商务的蓬勃发展极大地改变了传统的购物行为,越来越多的人愿意尝试通过互联网购物。本文提出了一个个性化购物模型,该模型采用代理技术来促进互联网购物自动化,提高购物效率,在代理技术的基础上,购物模型集成了基于知识的决策生成方法和偏好获取方法,进而为自动购物提供决策支持,最后,实现了一个用于轿车购物的实验原型系统以验证提出的购物模型。结论表明,本系统运行效率高,能够为利用互联网购物的客户节省大量时间。     

Smart shopper: an agent-based web-mining approach to Internet shopping

This paper presents an agent-based Web-mining approach to Internet shopping. We propose a fuzzy neural network to tackle the uncertainties in practical shopping activities, such as consumer preferences, product specification, product selection, price negotiation, purchase, delivery, after-sales service and evaluation. The fuzzy neural network provides an automatic and autonomous product classification and selection scheme to support fuzzy decision making by integrating fuzzy logic technology and the backpropagation feed forward neural network. In addition, a new visual data model is introduced to overcome the limitations of the current Web browsers that lack flexibility for customers to view products from different perspectives. Such a model also extends the conventional data warehouse schema to deal with intensive data volumes and complex transformations with a high degree of flexibility for multiperspective visualization and morphing capability in an interactive environment. Furthermore, an agent development tool named "Aglet" is used as a programming framework for system implementation. The integration of dynamic object visualization, interactive user interface and data mining decision support provides an effective technique to close the gap between the "real world" and the "cyber world" from a business perspective. The experimental results demonstrate the feasibility of the proposed approach for Web-based business transactions.     

网络交易中多Agent协商策略的研究

网络购物是当今社会发展的必然趋势,如何在丰富的网络资源中选择自己需求的商品达成交易是关键。因此根据Agent技术的特点,采用Agent技术对网络资源进行收集、选择、提取,获得用户满意的商品信息,并提出了一种多Agent协商策略模型,收到对方提出的意见后通过经验值和互助机制作出一定的判断,看是否达到预期的效果给出相应的反映。该模型主要通过经验值的积累,准确掌握对方的信息,制定出一套协商策略,采用利益随机调整方式选择对策,促进协商成功。经过实验证明,此算法有效。     

基于MVC架构文档报表管理系统的设计与实现

根据公司对文档报表管理的实际需求,通过对某房地产评估公司的日常报告文档管理的各方面的需求分析,将系统的需求抽象化、模块化、系统化,利用.NET开发平台MVC架构实现了报告信息管理、数据维护管理、公司收据和报告文档管理等功能,实现了管理文档的规范化和信息化,提高公司管理文档信息的工作质量、管理水平,为用户提供更为方便快捷的服务。     

基于SOA的多Agent协商服务及其应用

在简述一种融合了面向服务架构和多Agent协商系统特点的新的协商模型——基于面向服务架构的多Agent协商模型的基础上，设计了该协商模型的多Agent协商服务端，阐述了Agent中协商推理机在协商中的基本过程。面向服务架构的协商服务端与协商Agent管理平台组合起来，搭建起支持开放环境并开展协商的软件应用环境，实现了基于面向服务架构的多Agent协商模型在电子商务中的 应用。     

iJADE Web-miner: an intelligent agent framework for Internet shopping

There is growing interest in using intelligent software agents for a variety of tasks, including navigating and retrieving information from the Internet and from databases, online shopping activities, user authentication, negotiation for resources, and decision making. We propose an integrated framework for information retrieval and information filtering in the context of Internet shopping. We focus on applying agent technology, together with Web mining technology, to automate a series of product search and selection activities. It is based on a multiagent development platform, namely, iJADE (intelligent Java agent development environment), which supports various e-commerce applications. The framework comprises an automatic facial authentication utility and six other modules, namely, customer requirements definition, a requirement-fuzzification scheme, a fuzzy agents-negotiation scheme, a fuzzy product-selection scheme, a product-defuzzification scheme, and a product-evaluation scheme. A series of experiments were carried out and favorable results were produced in executing the framework. From an experimental point of view, we used a database of 1,020 facial images that were obtained under various conditions of facial expression, viewing perspective and size. An overall correct recognition rate of over 85 percent was attained. For the product selection test of our fuzzy shopper system, an average matching rate of more than 81 percent was achieved.