Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Hardcover)

Michael P. Wellman, Amy Greenwald, Peter Stone

商品描述

Description

E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry.

The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents--to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types--encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding.

Autonomous Bidding Agents provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors--who introduced TAC and created some of its most successful agents--offer both an overview of current research and new results.

商品描述(中文翻譯)

描述

電子商務越來越為自主競標代理提供機會:這是一種在電子市場上進行競標的電腦程式,無需直接人為干預。對於已知估值的單一商品的自動競標策略相對簡單;而對於具有相互依賴估值的同時競標,設計策略則更為複雜。本書介紹了從學術界和工業界最近的研究成果中出現的算法進展和策略思想,這些成果都是在這個快速發展的領域中取得的。

作者分析了幾種新穎的競標方法,這些方法是從自2000年以來每年舉辦的交易代理競賽(TAC)中發展出來的。這個競賽的基準挑戰是在不同類型的同時競標中以相互依賴的估值買賣多個商品,鼓勵競爭者對共同任務應用創新技術。本書追蹤了TAC的演變,並跟踪了從構思到多次競賽中選定的代理的過程,介紹並分析了為自主競標開發的詳細算法。

《自主競標代理》提供了這個快速發展的人工智能領域中方法的首個綜合處理。作者是TAC的創始人之一,也創建了一些最成功的代理,他們提供了對當前研究的概述和新的結果。