一种基于多Agent系统的在线广告竞价模型

针对在线广告竞价这一具体应用场景,本文在价高者得的传统竞价原则上,结合竞价双方的相似度阈值控制,提出一种适用于在线广告交易的竞价模型,即SMOBM(the Similarity-based Multi-agent Online-advertising Bidding Model)模型。该模型使用多Agent系统建模,根据市场角色设计相应Agent,并设计出需求聚合Agent组织和资源聚合Agent组织。买卖双方使用回合制的拍卖协商方法进行连续叫价,并在系统全局时钟的控制下产生交易结果。最后,使用Repast Simphony仿真软件对模型进行实现,将基于组织模型和不基于组织模型这两种情况进行对比,结果表明在选择合适的相似度阈值时,基于组织模型的交易成功率高,且在存在恶性竞争时整体交易表现出较好的鲁棒性。     

基于半量子秘密比较的量子拍卖协议

针对量子密封拍卖协议中报价隐私保护不足、第三方不可信、对参与双方量子能力要求较高等情况，提出了一种将高能级单粒子作为信息载体的基于半量子秘密比较的量子密封投标拍卖协议。协议过程无需第三方参与，且采用半量子方式，仅要求拍卖方为强量子能力方，投标方仅需拥有反射粒子及制备单粒子的能力。协议利用半量子秘密比较，实现对投标方报价的隐私保护，拍卖方仅能获得报价之间的大小关系，而无法获取具体报价。文中通过理论分析证明了所提协议具有较高的安全性，能够抵御测量-重发攻击、截获-重发攻击、纠缠攻击、共谋攻击等多种攻击，且协议通信效率较稳定，不受投标人数的影响。     

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。     

一种递增叫价的多属性拍卖方法

采用拍卖方法来进行资源分配是多Agent系统研究中的重要问题．基于广义的多属性拍卖模型，提出了一种递增叫价的多属性拍卖方法——MAE．对MAE中卖方和买方的策略和效用进行了分析，进而证明了MAE的一些重要性质．例如卖方和买方满足独立理性，且都有接近最优的策略，在这一组策略下买方和卖方的总效用接近最大．将MAE与已有的多属性拍卖方法进行了比较，结果表明MAE改进了Esther David的工作，并且说明MAE是一种可以取代MAV的递增叫价多属性拍卖方法．     

基于多Agent的合作伙伴选择协商模型

为提高合作伙伴选择的效率和质量，将具有自治性的软件Agent应用于合作伙伴选择系统，提出了基于多Agent的合作伙伴选择系统的框架。定义了Agent的结构，确定了Agent间交互作用的方式，包括协商目标、协商协议和推理模型。     

一种基于Bayesian信任模型的多Agent协作系统

提出了一种基于信任机制的多Agent协作系统的形成方案.通过借鉴社会学中的人际关系信任模型,建立Agent间的信任关系,利用Bayesian方法对Agent的可信度进行评估,通过Agent间基于信任关系的链接更新形成稳定的多Agent协作系统.仿真结果证实,提出的协作方案能有效地促进Agent间的协作效率,提高共同完成任务的成功率.     

一种基于移动代理的自主拍卖模型

Agent的自治性和移动性为网络环境下的电子商务活动提供了许多潜在优点.拍卖是一种典型的商务活动,基于Agent的自主拍卖是传统拍卖方式的一个发展趋势,具有广泛应用前景.重点研究了建立自主拍卖系统所涉及的主要问题,包括拍卖大厅建模、拍卖大厅查询、交易对象定位、拍卖过程同步和出价策略等.基于移动Agent和博奕理论,提出了一种自主拍卖模型.该模型支持一次价格密封标价拍卖和双边拍卖两种拍卖机制.模型中的Agent接受拍卖任务后能够自动搜寻拍卖大厅、自主参与拍卖过程并向其所有者返回拍卖结果.     

资源有限连续双向拍卖环境下的ZIP2策略

利用经济模型和多Agent技术研究网络资源分配问题已成为当前网络研究新的趋势.针对Agent资源有限的网络环境,提出了一种基于连续双向拍卖市场机制的ZIP2策略.ZIP2策略是一种包含价格和数量的二维报价策略,Agent采用该策略具有机器学习能力.最后通过模拟实验验证了ZIP2策略可以实现较高的网络资源分配效率,平均分配效率超过97%.     

云计算环境下兼顾买卖双方利益的双向拍卖资源分配算法

针对当前云计算环境下的资源分配算法不能充分考虑买卖双方利益的问题,本文提出了一种适用于云计算环境的组合双向拍卖资源分配模型。首先,初始化云经纪人列表和供应商报价列表,拍卖人通知拍卖参与者拍卖开始;然后,根据属性值按升序排序云经纪人请求和云服务供应商报价列表,从而确定投标获胜者;最后,获胜的云经纪人向相关云服务供应商发送任务并支付费用,云服务商执行任务。仿真实验使用CloudSim原型化,在基于Java的仿真云环境中从经济角度进行了效率评估。仿真结果表明,本文模型适用于云环境中的资源分配,在经济上非常有效。相比其他的现有模型,本文模型更能鼓励参与者在买卖双方公平公正的前提下根据真实估值竞购资源。     

基于ASP.NET技术的网上拍卖系统的设计

采用Browser/Server软件结构,运用ASP.NET技术及SQL Server数据库开发网上拍卖系统,实现网上拍卖。阐述了系统的框架结构、数据流程分析、功能模块设计以及数据库设计。系统为拍卖交易双方提供了一个稳定运行的平台,并实现即时报价、管理拍卖商品、用户管理、反馈价格、拍卖商品等主要功能。     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff’s ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.     

一种基于双边拍卖的复制优化策略

提出了基于连续双边拍卖协议的复制优化策略，解决数据网格中复制优化策略难以适应数据高度自治性和动态性的问题。首先给出了双边拍卖机制的实现模型，接着给出了分布式的连续双边拍卖协议算法和基于零智慧增强学习的投标策略，最后使用Optorsim对模型和复制优化策略进行仿真。实验结果表明，该复制优化策略与基于单边拍卖的优化策略相比，具有较低的任务运行时间和网络有效占用率,较好的存储和计算资源利用率。     

DECENTRALIZED SUPPLY CHAIN FORMATION USING MAX‐SUM LOOPY BELIEF PROPAGATION

Supply chain formation is the process by which a set of producers within a network determine the subset of these producers able to form a chain to supply goods to one or more consumers at the lowest cost. This problem has been tackled in a number of ways, including auctions, negotiations, and argumentation‐based approaches. In this paper we show how this problem can be cast as an optimization of a pairwise cost function. Optimizing this class of energy functions is NP‐hard but efficient approximations to the global minimum can be obtained using loopy belief propagation (LBP). Here we detail a max‐sum LBP‐based approach to the supply chain formation problem, involving decentralized message‐passing between supply chain participants. Our approach is evaluated against a well‐known decentralized double‐auction method and an optimal centralized technique, showing several improvements on the auction method: it obtains better solutions for most network instances which allow for competitive equilibrium (Competitive equilibrium in Walsh and Wellman is a set of producer costs which permits a Pareto optimal state in which agents in the allocation receive non‐negative surplus and agents not in the allocation would acquire non‐positive surplus by participating in the supply chain) while also optimally solving problems where no competitive equilibrium exists, for which the double‐auction method frequently produces inefficient solutions.     

资源有限连续双向拍卖环境下的理性策略

网络系统的动态资源分配是未来IT系统必须解决的一个基本问题。针对agent资源的有限性,提出了连续双向拍卖环境下（Continuous Double Auction,CDA）agent具有理性行为的GD2策略。GD2策略是一种包含价格和数量的二维报价策略,agent通过建立信任函数和计算最大期望利润调整报价,实验表明GD2策略可以实现较高的动态资源分配效率,平均分配效率超过98%。     

一种安全的多属性拍卖模型

网络拍卖不仅成为电子商务不可分割的部分,而且是自治Agent和多Agent系统(MAS)技术的重要应用领域.同时,拍卖作为一种有效的资源分配形式,在联盟生成等MAS问题中有着重要的应用,因而受到学者们越来越多的关注.拍卖的安全性、隐私性特征是拍卖研究的重要方面.给出了一个一般的多属性拍卖模型和一个安全的多属性拍卖协议--SVAMA协议,并证明了SVAMA的一些重要性质,如Strategy-proof等.并与已有的多属性拍卖和使用的安全方法进行了比较,部分改进了David,Brandt等人的工作.     

An expansion matching method to improve transaction effectiveness in the double auction market

The double auction is an important transaction mechanism in electronic commerce. Buyers and sellers can interact and be matched with each other in a double auction e-market. Consequently, enhancing the effectiveness of the double auction market to help traders successfully complete their transactions is an important issue. In this research study, Trading Agent Competition (TAC) data were collected to examine double auction market mechanisms. The TAC is a worldwide, renowned competition in which intelligent agents are employed to simulate business/market operations, and the TAC Market Design (CAT) tournament is an individual TAC competition that focuses on the double auction market. Thus, we conducted simulation experiments on the CAT competition platform, and the transaction data were analyzed to identify the impact of market design strategies on market performance, such as market share, market profit and transaction success rate. Based on these results, we developed an expansion matching method to enhance market performance, and we conducted verification experiments to evaluate our method. The results show that our expansion matching method promotes improved performance of market policies in the double auction market.     

应急物资采购及代储服务交易的逆向组合拍卖机制设计

建立市场化的政企联合储备模式已经成为应急物资储备体系建设的重要方式.基于此,着眼于应急物资采购及代储服务的交易问题,设计一个逆向组合拍卖机制.在此拍卖机制中,政府是拍卖的买方兼委托人,企业是拍卖的卖方兼竞拍者,应急物资采购及代储服务是拍卖商品.首先,通过一个报童模型建立政府决策行为与拍卖活动之间的关系,并提出企业的投标策略;其次,建立最小化供需偏差和最大化供给数量的竞胜标决定模型;最后,提出一个符合实际背景的数值算例对拍卖机制进行模拟和验证.研究表明,所提出的逆向组合拍卖机制不仅具有经济效率,还能够促进政府一次性达成与多家企业在多个周期的合作.由此可见,运用拍卖机制解决应急物资政企联合储备的交易问题具备理论的优越性和现实的适用性.     

一种基于双向拍卖机制的计算网格资源分配方法

针对计算网格资源的特点以及运用经济机制进行网格资源管理所具有的灵活性及有效性,提出一种改进的基于双向拍卖机制的网格资源分配方法.首先,描述了基于双向拍卖机制的资源分配框架,整个系统由买方、卖方和计算资源经纪人组成.然后,针对网格中的CPU资源,提出一种改进的双向拍卖机制,采用统一拍卖方式,可以灵活调节交易双方的付费.进而,分析了该双向拍卖机制满足优势策略激励相容、预算平衡以及个人理性的特点,并定义了拍卖机制的效率.最后,通过实验分析了双向拍卖分配机制的效率.