More Web Proxy on the site http://driver.im/

Article

Bidding in periodic double auctions using heuristics and dynamic Monte Carlo tree search

Authors:

Moinul Morshed Porag Chowdhury,

Christopher Kiekintveld,

William YeohAuthors Info & Claims

IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence

Pages 166 - 172

Published: 13 July 2018 Publication History

Abstract

In a Periodic Double Auction (PDA), there are multiple discrete trading periods for a single type of good. PDAs are commonly used in real-world energy markets to trade energy in specific time slots to balance demand on the power grid. Strategically, bidding in a PDA is complicated because the bidder must predict and plan for future auctions that may influence the bidding strategy for the current auction. We present a general bidding strategy for PDAs based on forecasting clearing prices and using Monte Carlo Tree Search (MCTS) to plan a bidding strategy across multiple time periods. In addition, we present a fast heuristic strategy that can be used either as a standalone method or as an initial set of bids to seed the MCTS policy. We evaluate our bidding strategies using a PDA simulator based on the wholesale market implemented in the Power Trading Agent Competition (PowerTAC) competition. We demonstrate that our strategies outperform state-of-the-art bidding strategies designed for that competition.

References

[1]

Cameron B. Browne, Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1-43, 2012.

[2]

Guillaume Chaslot, Sander Bakkes, Istvan Szita, and Pieter Spronck. Monte-Carlo Tree Search: A New Framework for Game AI. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2008.

Digital Library

[3]

Guillaume M. J. B. Chaslot, Mark H. M. Winands, Jos W. H. M. Uiterwijk, H Jaap van den Herik, and Bruno Bouzy. Progressive Strategies for Monte-Carlo Tree Search. New Mathematics and Natural Computation, 4(03):343-357, 2008.

[4]

Moinul Morshed Porag Chowdhury, Russell Y. Folk, Ferdinando Fioretto, Christopher Kiekintveld, and William Yeoh. Investigation of Learning Strategies for the SPOT Broker in Power TAC. In Proceedings of the International Workshop on Agent-Mediated Electronic Commerce and Trading Agents Design and Analysis (AMEC/TADA), pages 96-111, 2015.

[5]

Moinul Morshed Porag Chowdhury. Predicting Prices in the Power TAC Wholesale Energy Market. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 4204-4205, 2016.

Digital Library

[6]

Federal Energy Regulatory Commission, 2017.

[7]

European Energy Exchange, 2017.

[8]

Daniel Friedman. The Double Auction Market Institution: A Survey. In The Double Auction Market, pages 3-26. Routledge, 2018.

[9]

Dhananjay K. Gode and Shyam Sunder. Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality. Journal of Political Economy, 101(1):119-137, 1993.

[10]

Mark A. Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The WEKA data mining software: an update. SIGKDD Explorations, 11(1):10-18, 2009.

Digital Library

[11]

Wolfgang Ketter, John Collins, and Mathijs de Weerdt. The 2018 Power Trading Agent Competition. ERIM Report Series Reference No. 2017-016-LIS, 2018.

[12]

Levente Kocsis and Csaba Szepesvári. Bandit Based Monte-Carlo Planning. In Proceedings of the European Conference on Machine Learning (ECML), pages 282-293, 2006.

Digital Library

[13]

Rodrigue Talla Kuate, Minghua He, Maria Chli, and Hai H. Wang. An Intelligent Broker Agent for Energy Trading: An MDP Approach. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 234-240, 2013.

Digital Library

[14]

S. Ozdemir and Rainer Unland. AgentUDE: The Success Story of the Power TAC 2014 Champion. In Proceedings of the Workshop on Agent-Mediated Electronic Commerce and Trading Agents Design and Analysis (AMEC/TADA), 2015.

[15]

Nord Pool, 2017.

[16]

Gerald Tesauro and Jonathan L Bredin. Strategic Sequential Bidding in Auctions using Dynamic Programming. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 591-598, 2002.

Digital Library

[17]

Gerald Tesauro and Rajarshi Das. High-performance Bidding Agents for the Continuous Double Auction. In Proceedings of the ACM Conference on Electronic Commerce (EC), pages 206-209, 2001.

Digital Library

[18]

Daniel Urieli and Peter Stone. Tac-Tex'13: A Champion Adaptive Power Trading Agent. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1447-1448, 2014.

Digital Library

[19]

Daniel Urieli and Peter Stone. An MDP-based Winning Approach to Autonomous Power Trading: Formalization and Empirical Analysis. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 827-835, 2016.

Digital Library

[20]

Michael P. Wellman, Amy Greenwald, Peter Stone, and Peter R. Wurman. The 2001 Trading Agent Competition. Electronic Markets, 13(1):4-12, 2003.

[21]

Peter R. Wurman, William E. Walsh, and Michael P. Wellman. Flexible Double Auctions for Electronic Commerce: Theory and Implementation. Decision Support Systems, 24(1):17-27, 1998.

Digital Library

Cited By

Pedasingu BSubramanian EBichpuriya YSarangan VMahilong N(2020)Bidding Strategy for Two-Sided Electricity MarketsProceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3408308.3427976(110-119)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1145/3408308.3427976

Recommendations

Bidding Strategy for Periodic Double Auctions Using Monte Carlo Tree Search
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

Bidding strategies for Periodic Double Auctions (PDAs) are complicated because they need to predict and plan for future auctions, which may affect the bidding strategy in the current auction. We present a general bidding strategy for PDAs based on ...
Correcting vindictive bidding behaviors in sponsored search auctions

In this study, we aim to develop a pricing mechanism that reduces the effects resulted by vindictive advertisers who bid on sponsored search auctions run by search engine providers. In particular, we aim to ensure payment fairness and price stability in ...
Vindictive bidding in keyword auctions
ICEC '07: Proceedings of the ninth international conference on Electronic commerce

We study vindictive bidding, a strategic bidding behavior in keyword auctions where a bidder forces his competitor to pay more without affecting his own payment. We show that most Nash equilibria (NE) are vulnerable to vindictive bidding and are thus ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence

July 2018

5885 pages

ISBN:9780999241127

Editor:
Jérôme Lang

Sponsors

Adobe
IBMR: IBM Research
ERICSSON
Microsoft: Microsoft
AI Journal: AI Journal

Publisher

AAAI Press

Publication History

Published: 13 July 2018

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pedasingu BSubramanian EBichpuriya YSarangan VMahilong N(2020)Bidding Strategy for Two-Sided Electricity MarketsProceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3408308.3427976(110-119)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1145/3408308.3427976

View Options

View options

Media

Figures

Other

Tables

View Table of Contents