Meta-learning of bidding agent with knowledge gradient in a fully agent-based sponsored search auction simulator

Donghun Lee, Warren B. Powell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We take a practical approach on learning how to bid in sponsored search auctions, and model the problem of improving real world profit of advertisers in sponsored search auction as a meta-learning problem of configuring adaptive bidding agents. We construct a fully agent-based sponsored search auction simulator that 1) captures the dynamic nature of sponsored search auctions, 2) emulates the interface of Google AdWords platforms, and 3) can be customized and extended by modules. We then present Meta-LQKG algorithm, an agent-based meta-learning algorithm using knowledge gradient, and show the effect of meta-learning with Meta-LQKG on the performance of adaptive bidding agents.

Original languageEnglish (US)
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages2090-2092
Number of pages3
ISBN (Electronic)9781510892002
StatePublished - Jan 1 2019
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: May 13 2019May 17 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume4
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
CountryCanada
CityMontreal
Period5/13/195/17/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Keywords

  • , Agent-Based Simulation
  • Knowledge gradient
  • Learning to bid
  • Meta-learning
  • Sponsored search auction

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  • Cite this

    Lee, D., & Powell, W. B. (2019). Meta-learning of bidding agent with knowledge gradient in a fully agent-based sponsored search auction simulator. In 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 (pp. 2090-2092). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 4). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).