Walverine: a Walrasian trading agent

被引：15

作者：

Cheng, SF ^{[1
]}

Leung, E ^{[1
]}

Lochner, KM ^{[1
]}

O'Malley, K ^{[1
]}

Reeves, DM ^{[1
]}

Schvartzman, JL ^{[1
]}

Wellman, MP ^{[1
]}

机构：

[1] Univ Michigan, Artificial Intelligence Lab, Ann Arbor, MI 48109 USA

来源：

DECISION SUPPORT SYSTEMS | 2005年 / 39卷 / 02期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1016/j.dss.2003.10.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments. (c) 2003 Elsevier B.V. All rights reserved.

引用

页码：169 / 184

页数：16

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