Learning and reaction algorithms for certain decision functions used in electronic negotiation

尽管在2000年出现了衰退，电子商务（电子商务）仍然是全球经济的一个重要和不断增长的部分，研究表明，在过去的几年里，每年的销售额增长超过20%。电子商务带来的一个变化是在谈判领域。研究人员已经设计了一些系统，被称为电子谈判（EN）系统，它使用计算机网络来促进谈判各方之间的信息/提议的交换。由于在线市场的迅速普及，网络谈判正日益成为传统面对面谈判的重要竞争对手。源源不断的研究文献证明了人类谈判者在了解对手偏好后所获得的进步。然而，英语学习方法的发展在某种程度上还处于起步阶段。本研究将提出EN系统学习对手谈判策略的方法，并利用这些知识以最佳方式做出反应。我们将把我们的方法应用于特定的业务情况，并开始开发可用于设计健壮的EN系统的框架。电子商务系统的发展对电子商务行业非常重要，这项研究将为新兴领域的人员提供高级培训。

Despite its downturn in 2000, electronic commerce (e-commerce) remains an essential and growing part of the global economy, with studies indicating an annual increase in sales of over 20% during the last few years.  One change that is brought about by e-commerce is in the area of negotiation. Researchers have designed a number of systems, known as Electronic Negotiation (EN) systems, which use computer networks to facilitate the exchange of information/offers between the negotiating parties. Owing to the rapid adoption of online markets, EN is becoming an increasingly important competitor to traditional face-to-face negotiation.  A steady stream of research literature has attested to the improvements afforded to human negotiators on account of learning an opponent's preferences.  However, the development of learning methodologies for the EN context is somewhat in its infancy.  This research will propose methods for an EN system to learn an opponent's negotiation tactics and to use that knowledge to react in an optimal manner.  We will apply our methodologies to specific business situations, and also start to develop a framework that can be used to design robust EN systems.  The development of EN systems will be important to the e-commerce industry, and this research will provide for the advanced training of personnel in an emerging area.