发帖
雷达卡
摘要翻译：
拍卖正在成为一种越来越流行的交易方式，尤其是在互联网上。本文提出了一种构建自治投标代理的通用方法，用于在多个同时拍卖中对相互影响的货物进行投标。我们方法的一个核心组成部分是学习一个基于过去数据的经验价格动态模型，并使用该模型分析计算，最大可能的最优出价。针对这类条件密度估计问题，即监督学习问题，我们提出了一种新的通用的基于Boosting的算法，其目标是估计实值标号的整个条件分布。在第二届交易代理大赛(TAC-01)中，该方法在ATTac-2001中得到了充分的实现。我们给出的实验证明了我们的基于Boosting的价格预测器相对于几个合理的替代方案的有效性。
---
英文标题：
《Decision-Theoretic Bidding Based on Learned Density Models in
Simultaneous, Interacting Auctions》
---
作者：
J. A. Csirik, M. L. Littman, D. McAllester, R. E. Schapire, P. Stone
---
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：
Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.
---
PDF链接：
https://arxiv.org/pdf/1106.5270
京公网安备 11010802022788号
