【作者】 孙天昊；

【导师】 朱庆生；

【作者基本信息】 重庆大学 ， 计算机系统结构， 2008， 博士

【摘要】 目前电子商务已经走进寻常百姓家中,人们愿意并慢慢习惯去淘宝网(www.taobao.com)等购物网站购买商品。中国互联网数据中心(DCCI)于2008年7月15日公布:2008年上半年中国互联网用户互联网消费规模为2560亿元,同比增长58.2%。目前的购物网站中采用的定价方式有:一口价、拍卖和代理出价。这些都属于半智能化商务活动,一口价方式过于简单,拍卖方式需要实时的人机交互,代理出价相对简单且风险较大。更重要的是这些方式都是单向的,没有把传统商务中的最具智慧的、灵活多变的、双向交互的协商机制引入进来。分布式人工智能中Agent技术的发展和逐步成熟为电子商务的自动化和智能化提供了技术保障。基于Agent的电子商务极大的提高了电子商务自动化和智能化程度,将进一步推动电子商务的发展。协商作为商务活动中的一至关重要的组成部分,它的结果直接影响商务活动的继续执行。自动协商是基于Agent系统的一种重要交互方式,目前协商问题是多Agent系统研究的热点。根据协商活动中参与者的数量,自动协商分为3类:一对一协商、一对多协商和多对多协商。一对一协商最早被研究、比较简单;一对多协商被处理为多个并发的一对一协商;多对多协商可以由多个一对多协商扩展而来。本文研究电子商务中一对多协商模式。主要的研究内容和创新工作包括:①一对多协商模型研究在现有的一对多协商模型研究基础上,提出一个更加灵活的连续一对多协商模型。该模型支持连续协商,满足开放和动态的协商环境。与一般的一对多协商模型相比,我们主要在三个方面进行了改进:1)支持连续协商,节约协商时间,提高协商效率。协商线程不需要一直等到接收完了所有卖家Agent的提议之后才能够生成反提议,即协商线程之间不需要相互等待。2)满足开放和动态的协商环境要求,适应性更强。在协商过程中,新的卖家可以加入,现有卖家可以撤离。3)使用基于相对效用的协调策略来选择最优的协定,达成协定的效用更高,成本更低。②让步型协商策略研究为了进一步提高和优化让步协商策略(基于时间的协商策略、基于对手行为的协商策略等)达成的初步协商解的质量,提出了基于等效置换的协商策略。等效置换充分利用多议题协商效用评估机制中各议题之间的相关性,在保证协商者既得利益的前提下动态改变某些议题的取值,促使协商双方得到更优的协商解,并同时提高联合效用。③学习型协商策略研究把机器学习方法引入Agent中,赋予Agent学习能力,使Agent具有更强的智能性。论文分别对贝叶斯学习、增强学习、遗传算法等几种主流的学习机制进行研究。并对基于增强学习的协商策略进行优化,在协商过程中充分利用对手的历史信息,综合增强学习和对手历史学习策略,加快协商解的收敛和提高协商解的质量。④协调策略研究一对多协商可以看成是多个并发的一对一协商,这就需要一个协调者使用协调策略来管理这些并发的协商。协调策略确保多个并发的一对一协商能够有效、有序和健壮的进行。在研究了现有协调策略(孤注一掷策略、耐心策略、最优耐心策略、策略操纵策略、固定等待时间策略、固定等待率策略等)基础上,提出了基于相对效用的协商协调策略,该策略能够很好地解决当多个并发协商进程同时获得满足效用评估的提议,特别是存在多个相同最大效用提议时的取舍问题,确保得到效用更高,成本更低的协商解。⑤最佳卖家撤离管理机制研究在现有一对多协商模型的基础上分析了在协商过程中当前最佳卖家撤离对系统的影响,包括最终协商解的效用下降、协商时间延长等,提出了三种协调策略:策略1:不管,继续执行(Let it be);策略2:以次最佳卖家的信念继续执行(Second);策略3:重新开始(Restart)。建立具有承诺管理机制的一对多协商模型来制约当前最佳卖家的撤离,改善系统性能,并给系统提供了更加灵活的机制。论文对一对多协商的几个主要方面进行了全面研究,包括协商模型、协商策略、协调策略和管理机制。既继承了前人的研究结果,又分别进行了扩展和创新,具有前沿性、理论价值和实用意义。更多还原

【Abstract】 Nowadays, Electronic Commerce has already come into our homely lives. People are willing to go to shopping at some shopping websites, such as taobao (www.taobao.com), and slowly form a habit. On July 15, 2008, Data Center of the China Internet (DCCI) published the research data for the first half of 2008, which shows that the Internet consumption of the Chinese Internet users was 256 billion, increased by 58.2% as compared with the same period last year. But the typical pricing methods of these shopping websites are the fixed price, auction and bid by agent, which are all semi-intelligent. Fixed price is too simple; Auction needs real time interaction between men and machine; Bid by agent is relative simple and has a large risk. Furthermore, these methods are all unilateral, while negotiation mechanism is most sapiential, flexible and bilateral.With the development and increasingly ripeness of Agent technology of Distributed Artificial Intelligence (DAI), it is possible to make Electronic Commerce automatic and intelligent. Electronic Commerce based on agent enhances the automatization and intelligence of business process, which will improve the development of Electronic Commerce. Negotiation is a most important phase of business process, directly affects sequent execution of whole business process. Automated negotiation is a key form of interaction in agent-based systems. Now negotiation is a hot research of Multiple Agent System (MAS). In terms of the number of agents participating in negotiation, agent-based automated negotiation can be divided into three cases: one-to-one negotiation (bilateral negotiation), one-to-many negotiation and many-to-many negotiation. One-to-one negotiation is researched earliest of these three cases; one-to-many negotiation can be seen as multiple concurrent one-to-one (bilateral) negotiations; many-to-many negotiation can be extended from multiple one-to-many negotiation.This paper researches on one-to-many negotiation in Electronic Commerce. The following are the main research contents and the innovations.①Research on one-to-many negotiation modelBased on research of the existing general one-to-many negotiation model, a more flexible one-to-many negotiation model is proposed. This model supports continuous, open and dynamic negotiation. Compared with the general model, the advantages of our flexible model are: 1) Our model supports continuous negotiation which can decrease negotiation time, improve negotiation efficiency. Buyer agent should not wait until having received offers from all its trading partners before generating counteroffers, that is, negotiation threads need not wait each other. 2) Our model satisfies the requirement of open and dynamic settings. In the middle of negotiation, new seller agent can join, and the existing seller can leave at any time. 3) Our model uses the coordination strategy based on relative utility to select the best agreement which has the higher utility and lower cost.②Research on concession negotiation strategiesIn order to enhance and optimize the negotiation result reached by concession negotiation strategies (Time-based negotiation strategy, opponent-behavior-based negotiation strategy), equal-utility exchange negotiation strategy is proposed based on the concession negotiation strategies. Equal-utility exchange makes use of the relationship between the agent’s negotiation-related issues of the multi-issues utility evaluation mechanism. In order to get a better negotiation result, values of some issues are adjusted, ensuring the utility of agent not lower. Equal-utility exchange negotiation strategy can enhance joint utility partly and reach a better negotiation result than concession negotiation strategies.③Research on learning negotiation strategiesImporting machine learning to agent can make agent have the ability of learning, reasoning and intelligence. This paper firstly researches these popular machine learning, such as Bayesian learning, reinforcement learning and genetic algorithm. Then this paper proposes an optimized negotiation strategy based on reinforcement learning. In the middle of negotiation process, it makes the best use of the opponent’s negotiation history, in order to quicken the negotiation result convergence and enhance the negotiation result quality.④Research on coordination strategiesOne-to-many negotiation can be look as multiple, concurrent one-to-one (bilateral) negotiations. This needs a coordinator use coordination strategies to coordinate these multiple, concurrent one-to-one negotiations in one-to-many negotiation. Firstly,this paper researches several existing coordination strategies: Desperate Strategy, Patient Strategy, Optimized Patient Strategy, Strategy Manipulation Strategies, Fixed-Waiting-Time-Based Strategy, and Fixed-Waiting-Ratio-Based Strategy. Then relative utility theory is proposed. Next, coordination strategy based on relative utility is proposed. This coordination strategy can deal well when multiple, concurrent one-to-one negotiations all satisfy utility evaluation, especially having multiple maximum same utility offers at the same time. Coordination strategy insures multiple, concurrent one-to-one negotiations get the best negotiation result which has high utility and low cost.⑤Research on management mechanism for the best seller withdrawingFirstly, influence of the current best seller withdrawing is analyzed based on the existing one-to-many negotiation model, including decrease of final utility value and prolonging of average negotiation time; and three coordinating strategies were proposed. Then, one-to-many negotiation model with commitment management which used to restrict the current best seller withdrawing was created. Commitment management could reduce the probability of withdrawing of the current best seller, decrease influence of system performance and provide a more flexible mechanism for one-to-many negotiation model.This paper thoroughly researches several main aspects of one-to-many negotiation, including negotiation model, negotiation strategy, coordination strategy, and management mechanism. Our researches not only succeed to the research results of predecessor, but also develop the research and get innovations, which are frontier , and of theoretical and practical value.更多还原