The Trading Agent Competition

贸易代理大赛

• 批准号: 0624886
• 负责人: Amy Greenwald
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0624886&HistoricalAwards=false
• 关键词: Trading; Agent; Competition

This is a conference-related grant to support the travel, subsistence and registration expenses of approximately 16 student participants in a trading agent competition, being held in Hakodate, Japan, plus providing computational resources to enable educational experiences. The Trading Agent Competition (TAC) is an international forum designed to promote and encourage high-quality research about trading agents. TAC provides a platform for researchers to evaluate programmed trading techniques by competing with agents from other design groups in a simulated market scenario. TAC tournaments have been held annually since 2000, and have attracted participants from institutions in dozens of countries around the world. Involvement of American students will not only advance their individual careers in computer science, but will also contribute to the nation's future science and technology workforce. This activity contributes to curriculum development by developing and demonstrating methods for involving students in the design and competitive use of artificial agents.Entries in the Trading Agent Competition are software programs designed to trade in electronic markets. They are called "agents" because these programs operate autonomously in the market: sending bids, requesting quotes, accepting offers, and generally negotiating deals according to market rules. Although the agent's activity is ultimately determined by its programmers, the trading behavior is itself fully automated: humans do not intervene while the negotiation is in progress. Trading agents face a most challenging task. To play the market effectively, an agent must make real-time decisions in an uncertain and fast-changing environment, taking into account the actions of other agents that are doing the same. Capable agents rapidly assimilate market information from many sources, forecast future events, optimize complex offers and resource allocations, anticipate strategic interactions, and learn from experience. Successful trading agents adopt and extend state-of-the-art techniques from artificial intelligence, operations research, statistics, and other relevant fields. The annual trading agent competitions were initiated to promote research and education in the technology underlying trading agents. At the annual competition, the developers of techniques in trading strategy evaluate these ideas and communicate their results in a public forum for the benefit of the broader research community. The educational function of TAC is manifest by the significant role of students on almost all teams entering the competition. Many universities use TAC as an exercise for teaching about electronic commerce and artificial intelligence techniques. TAC is a useful aid in the classroom because it is by nature a hands-on (laboratory-like) experience for computer scientists. Research and education in trading agents promises to improve the state of art and practice in their development, and ultimately lead to more effective electronic markets. Equally important, increasing public knowledge in this area promotes understanding of the behavior of autonomous software agents as such systems become more prevalent in commerce and other domains.

这是一笔与会议有关的赠款，用于支助约16名学生参加在日本函馆举行的贸易代理人竞赛的旅费、生活费和注册费，并提供计算资源，以促进教育经验。贸易代理人竞赛（TAC）是一个国际论坛，旨在促进和鼓励有关贸易代理人的高质量研究。TAC为研究人员提供了一个平台，通过在模拟市场场景中与其他设计团队的代理人竞争来评估程序化交易技术。自2000年以来，TAC锦标赛每年举办一次，吸引了来自世界各地数十个国家的机构的参与者。美国学生的参与不仅将促进他们在计算机科学方面的个人职业生涯，而且还将为国家未来的科学和技术劳动力做出贡献。 这项活动通过开发和演示让学生参与人工代理的设计和竞争使用的方法来促进课程开发。交易代理竞争中的竞争对手是设计用于电子市场交易的软件程序。它们被称为“代理人”，因为这些程序在市场上自主运作：发送报价，请求报价，接受报价，并根据市场规则进行交易谈判。虽然代理的活动最终由其程序员决定，但交易行为本身是完全自动化的：在谈判过程中，人类不会干预。贸易代理人面临着最具挑战性的任务。为了有效地参与市场，代理人必须在不确定和快速变化的环境中做出实时决策，并考虑其他代理人的行为。有能力的代理商能够迅速从多个来源吸收市场信息，预测未来事件，优化复杂的报价和资源分配，预测战略互动，并从经验中学习。成功的交易代理商采用并扩展了人工智能、运筹学、统计学和其他相关领域的最先进技术。一年一度的交易代理竞赛旨在促进交易代理技术的研究和教育。在年度竞赛中，交易策略技术的开发者评估这些想法，并在公共论坛上交流他们的结果，以使更广泛的研究界受益。TAC的教育功能体现在学生在几乎所有参赛队伍中的重要作用。许多大学使用TAC作为电子商务和人工智能技术教学的练习。TAC在课堂上是一种有用的辅助工具，因为它本质上是计算机科学家的动手（类似实验室）体验。对交易代理人的研究和教育有望提高其发展的技术水平和实践水平，并最终导致更有效的电子市场。同样重要的是，在这一领域不断增加的公共知识促进了对自主软件代理行为的理解，因为这样的系统在商业和其他领域变得越来越普遍。