基于信任和K臂赌博机问题选择多问题协商对象

Agent之间的多问题协商(multi-issue negotiation)是一个复杂的动态交互过程.解决协商之前的对象选择问题在电子商务中有着重要的应用价值.为了提高多问题协商的准确性和购物Agent的效用,主要解决协商前的销售Agent的选择问题.为了充分利用协商历史,实现探索(exploration)和利用(exploitation)的折衷,把销售Agent的选择问题转变成K臂赌博机问题(K-armed bandit problem)来求解.提出了信任和声誉的度量模型,结合K臂赌博机问题的求解技术,采用学习机制,提出了几个确定奖励分布的改进算法.最后,以模拟协商过程为基础,将改进算法、信任和声誉有机地结合起来,提高了选择销售Agent的准确性和实用性.几个实验都说明了该工作在应用中的有效性.     

基于学习的多Agent多议题协商优化研究

以买方Agent的观点,对从交易平台上获得的卖方Agent的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,针对现有协商模型中存在的问题,提出了一个Agent协商对手选择算法和相应的交互机制,并验证了其可行性。该算法可用于Agent协商开始前协商对手的选择和初始信念的更新,对Agent在协商中策略的选择和执行具有指导作用,能有效提高Agent在协商中的效用及效率。     

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。     

一种基于信任的多Agent供应链协商关系网

Agent技术已被广泛用于供应链伙伴的协商。协商前如何选择协商Agent对提高协商效率有着重要的意义。提出了一种基于信任的多Agent协商关系网及其形成和更新算法,并对该协商关系网的特点进行了深入的研究。模拟表明,提出的协商关系网能有效地促进Agent之间的协商,提高协商成功率。     

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。     

对抗环境下多Agent协商问题的研究

利用多Agent系统具有的自治性和实时反应性,探讨对抗环境下的多Agent协商决策问题,提出一种混合式的多Agent结构协商模型,给出以最大团队效益为前提的协商求解策略和协商角色交换算法。通过协商,对抗环境中的Agent成员能够很好地进行动作策略选择和移动,能更好地进行进攻和防守。仿真实验验证了算法的可行性和有效性,结果表明其在一定程度上解决了多Agent系统中实时动态和受限通信对抗环境下的多Agent决策与合作问题。     

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。     

基于多Agent的网格信任模型

在对相关信任模型研究的基础上,提出了一个基于多Agent的信任模型,该模型采用在每个管理域设置多个Agent,每个实体和多个Agent关联的方式来管理网格环境中实体间的信任关系.实验和分析结果表明,与其它基于声誉的信任模型相比,这种模型不但缩短了交易响应时间,而且提高了作业成功率,从而能更加有效地解决网格环境中存在的安全问题.     

基于贝叶斯的多议题协商优化

在限时条件下的Agent之间的多议题协商中,虽然最差的结果是没有达成协定,而达成了一个使自己潜在利益受损的协定未必就是好的选择。在很多情况下,由于推理策略和交互机制的不完善使得Agent个体失去自己应得的利益。论文使用贝叶斯方法对协商对手进行预测,尽量使自己的初始信念准确反映对手的意识形态;并在此基础之上提出了一个优化的协商交互模型。在此模型中,Agent个体充分利用自己的预测结果,在协商成功的基础上获得尽可能多的利益。     

基于信任和声誉的Agent组织信誉模型

Agent组织是OOP(Object Oriented Programming)向AOP(Agent Oriented Programming)转变的重要形式,一个Agent组织要良好运行应该具有较高的信誉值,但此前这方面研究较少。针对上述问题,基于信任和声誉提出一种Agent组织信誉模型AOCM(Agent Organizational Credit Model)和计算方法,内部信任和外部声誉集成为Agent组织信誉,内部信任基于Agent之间的交互,外部声誉基于Agent的观察;改进了Dong Huynh等人关于Agent信任等方面的工作。实验结果验证了该模型的计算可行性、合理性。     

A Reputation–Oriented Reinforcement Learning Strategy for Agents in Electronic Marketplaces

In this paper, we propose a reputation–oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low–quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. Modeling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. As detailed in the paper, we believe that our proposed strategy leads to improved satisfaction for buyers and sellers, reduced communication load, and robust systems. In addition, we present preliminary experimental results that confirm some potential advantages of the proposed algorithm, and outline planned future experimentation to continue the evaluation of the model.     

一种直接评价节点诚信度的分布式信任机制

基于信誉的信任机制能够有效解决P2P网络中病毒泛滥和欺诈行为等问题.现有信任机制大多采用单个信誉值描述节点的诚信度,不能防止恶意节点用诚信买行为掩盖恶意卖行为;而且从信誉值上无法区分初始节点和恶意节点.提出一种新的分布式信任机制,基于交易历史,通过迭代求解,为每个节点计算全局买信誉值和卖信誉值,根据信誉值便能判断节点的善恶.仿真实验对比和性能分析表明,与EigenTrust算法相比,该算法能够迅速降低恶意节点的全局信誉值,抑制合谋攻击,降低恶意交易概率.     

Protecting buying agents in e-marketplaces by direct experience trust modelling

In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer goods with high quality and later offer the same goods with very low quality). In our approach, buying agents use a combination of reinforcement learning and trust modelling to enhance their knowledge about selling agents and hence their opportunities to purchase high value goods in the marketplace. This paper focuses on presenting the theoretical results demonstrating how the modelling of trust can protect buying agents from dishonest selling agents. The results show that our proposed buying agents will not be harmed infinitely by dishonest selling agents and therefore will not incur infinite loss, if they are cautious in setting their penalty factor. We also discuss the value of our particular model for trust, in contrast with related work and conclude with directions for future research.     

基于社会网络与声誉信任机制的移动多Agent系统信任模型

基于信任机制的移动多Agent系统中,代理Agent一般通过直接信誉值和推荐信誉值来判断对于另一个Agent的信任程度。由于系统相对巨大,直接信誉值通常难以获得,判断的正确性很大程度上依赖于推荐信誉值的准确性和可靠性。通过对整个多Agent系统进行社会网络的挖掘,用以得到与代理Agent存在潜在社会关系的一组Agent。对这组Agent提供的推荐信息充分信任,并优先使用这些Agent提供的信息进行推荐信誉值的计算。最后通过双方直接交易的多寡判断综合信任值中直接信誉值与推荐信誉值的权重。通过实验验证了该模型的有效性。     

Evaluating transaction trust and risk levels in peer-to-peer e-commerce environments

Trust evaluation is critical to peer-to-peer (P2P) e-commerce environments. Traditionally the evaluation process is based on other peers' recommendations neglecting transaction amounts. This may lead to the bias in transaction trust evaluation and risk the new transaction. The weakness may be exploited by dishonest sellers to obtain good transaction reputation by selling cheap goods and then cheat buyers by selling expensive goods. In this paper we present a novel model for transaction trust evaluation, which differentiates transaction amounts when computing trust values. The trust evaluation is dependent on transaction history, the amounts of old transactions, and the amount of the new transaction. Therefore, the trust value can be taken as the risk indication of the forthcoming transaction and is valuable for the decision-making of buyers.     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

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.     

Equilibrium of E-markets populated with reputation oriented learning agents

In this paper, we propose reputation oriented reinforcement learning algorithms for buying and selling agents in electronic marketplaces. We consider the fact that the quality of a good offered by multiple selling agents may not be the same, and a selling agent may alter the quality of its goods. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable and disreputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and optionally altering the quality of their goods. This paper focusses on presenting results from experiments investigating the behaviour of an e-market populated with our buying and selling agents. Our results show that such a market can reach an equilibrium state where the agent population remains stable, and this equilibrium is optimal for the participant agents. Thomas Tran, Ph.D.: He is an Assistant Professor in the School of Information Technology and Engineering at the University of Ottawa. He received his Ph.D. from the University of Waterloo in 2004. His current research work is on Multi-Agent Systems, Intelligent Agents, Reinforcement Learning, Trust and Reputation Modelling, Agent Negotiation, Mechanism Design and Applications of AI to E-Commerce. Robin Cohen, Ph.D.: She is a Professor in the School of Computer Science at the University of Waterloo. She received her Ph.D. from the University of Toronto in 1983. Her current research work is on User Modeling, Intelligent Interaction, Multi-Agent Systems, Adjustable Autonomy and Mixed-Initiative Systems and Dialogue, including Applications to E-Commerce.     

TRUST AS A TRADABLE COMMODITY: A FOUNDATION FOR SAFE ELECTRONIC MARKETPLACES

In large electronic marketplaces populated by buying and selling agents, it is difficult to judge trustworthiness. A variety of systems have been proposed to help traders to find trustworthy partners by learning to discount or disregard disreputable parties. In this article, we present a novel model for providing safe electronic marketplaces: Commodity Trunits, a system that considers trust as a tradable commodity. In this system, sellers require units of trust (trunits) to participate in transactions, and risk losing trunits if they act dishonestly. Sellers can purchase trunits when needed, and sell excess quantities. We demonstrate that under Commodity Trunits, rational sellers will choose to be honest, since this is the profit maximizing strategy. We also show that Commodity Trunits provides protection from a number of vulnerabilities common in existing trust and reputation systems, e.g., the important exit problem, where sellers can cheat without fear of repercussions if they intend to leave the market. We then present a simulation that validates the system by demonstrating that a market operator can manage the trunit marketplace to ensure sustainability. We conclude with a discussion of the value of Commodity Trunits as a method for promoting trust in electronic marketplaces.