[计算机科学] 交易代理竞争中的价格预测 [推广有奖]

摘要翻译：
2002年的贸易代理竞争(TAC)在旅游购物领域呈现了一场充满挑战的市场博弈。这一领域的关键问题之一是酒店价格的不确定性，它对替代旅行时间表的相对成本有重大影响。因此，几乎所有的参与者都使用某种方法来预测酒店价格。我们调查了锦标赛中使用的方法，发现代理人应用了有趣的多样性技术，考虑到与价格有关的不同证据来源。根据TAC-02决赛和半决赛中参赛者提供的关于其代理人实际预测的数据，我们分析了这些方法的相对有效性。结果表明，考虑关于航班价格的博弈特定信息是一个主要的区别因素。机器学习方法有效地从博弈数据中归纳出航班和酒店价格之间的关系，而基于竞争均衡分析的纯分析方法在没有历史数据的情况下也达到了同等的精度。采用了一种新的预测质量的衡量标准，我们将绝对准确性与游戏中的底线表现联系起来。
---
英文标题：
《Price Prediction in a Trading Agent Competition》
---
作者：
K. M. Lochner, D. M. Reeves, Y. Vorobeychik, M. P. Wellman
---
最新提交年份：
2011
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.
---
PDF链接：
https://arxiv.org/pdf/1107.0034