代理商竞争中的价格预测
Price Prediction in a Trading Agent Competition
摘要：
2002年的贸易代理商竞争（TAC）在旅游购物领域提出了一个具有挑战性的市场游戏。在这一领域的关键问题之一是酒店价格的不确定性，这对替代行程计划的相对成本有重大影响。因此，几乎所有的参与者都采用某种方法来预测酒店价格。我们调查的方法中采用的比赛，发现代理商应用一个有趣的多样性的技术，考虑到不同来源的证据对价格的影响。基于参赛者提供的数据，他们的代理人的实际预测在TAC-02决赛和半决赛，我们分析了这些方法的相对有效性。结果表明，考虑到游戏的具体信息的航班价格是一个主要的区别因素。机器学习方法有效地从游戏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能达到同样的准确性。采用一种新的预测质量衡量标准，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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
