RoxyBot-06：TAC出行的随机预测与优化
RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel
摘要：
在本文中，我们描述了我们的自主投标代理，RoxyBot，谁出现在2006年的贸易代理竞争的旅游部门在照片完成的胜利。在高层次上，许多成功的交易代理人的设计可以总结如下：（i）价格预测：建立市场价格模型;（ii）优化：在给定模型的情况下，求解近似最优的一组出价。为了进行预测，RoxyBot通过模拟同时上涨的拍卖建立了一个市场价格的随机模型。为了进行优化，RoxyBot依赖于样本平均近似方法，这是一种随机优化技术。
英文摘要：
In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged victorious in the travel division of the 2006 Trading Agent Competition in a photo finish. At a high level, the design of many successful trading agents can be summarized as follows: (i) price prediction: build a model of market prices; and (ii) optimization: solve for an approximately optimal set of bids, given this model. To predict, RoxyBot builds a stochastic model of market prices by simulating simultaneous ascending auctions. To optimize, RoxyBot relies on the sample average approximation method, a stochastic optimization technique.
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。
