David Pardoe
Ph.D. Alumni
David's research focuses on applications of machine learning in e-commerce settings. This research was motivated by his participation in the Trading Agent Competition, where he designed winning agents in supply chain management and ad auction scenarios. His dissertation explored methods by which agents in such settings can adapt to the behavior of other agents, with a particular focus on the use of transfer learning to learn quickly from limited interaction with these agents.
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A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations 2011
David Pardoe and Peter Stone, In Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011), May 2011.
Adaptive Trading Agent Strategies Using Market Experience 2011
David Merrill Pardoe,
A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations 2010
David Pardoe and Peter Stone, In EC 2010 Workshop on Trading Agent Design and Analysis (TADA), Cambridge, Massachusetts 2010.
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge 2010
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, and Kerem Tomak, Informs Journal on Computing, Vol. 22, 3 (2010), pp. 353-370.
Boosting for Regression Transfer 2010
David Pardoe and Peter Stone, In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), June 2010.
TacTex09: A Champion Bidding Agent for Ad Auctions 2010
David Pardoe, Doran Chakraborty, and Peter Stone, In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), May 2010.
The 2007 TAC SCM Prediction Challenge 2008
David Pardoe and Peter Stone, In AAAI 2008 Workshop on Trading Agent Design and Analysis 2008.
Adapting Price Predictions in TAC SCM 2007
David Pardoe and Peter Stone, In AAMAS 2007 Workshop on Agent Mediated Electronic Commerce 2007.
An Autonomous Agent for Supply Chain Management 2007
David Pardoe and Peter Stone, In Handbooks in Information Systems Series: Business Computing, Gedas Adomavicius and Alok Gupta (Eds.), Vol. 3, pp. 141-72 2007. Emerald Group.
Adaptive Mechanism Design: A Metalearning Approach 2006
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, and Kerem Tomak, In The Eighth International Conference on Electronic Commerce, pp. 92-102, August 2006.
Predictive Planning for Supply Chain Management 2006
David Pardoe and Peter Stone, In Proceedings of the International Conference on Automated Planning and Scheduling, June 2006.
TacTex-2005: A Champion Supply Chain Management Agent 2006
David Pardoe and Peter Stone, In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1489-94, July 2006.
Bidding for Customer Orders in TAC SCM 2005
David Pardoe and Peter Stone, In Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC 2004), P. Faratin and J.A. Rodriguez-Aguilar (Eds.), Vol. 3435, pp. 143-157, ...
Developing Adaptive Auction Mechanisms 2005
David Pardoe and Peter Stone, SIGecom Exchanges, Vol. 5, 3 (2005), pp. 1-10.
Evolving Neural Network Ensembles for Control Problems 2005
David Pardoe, Michael Ryoo, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2005.
TacTex-03: A Supply Chain Management Agent 2004
David Pardoe and Peter Stone, SIGecom Exchanges: Special Issue on Trading Agent Design and Analysis, Vol. 4, 3 (2004), pp. 19-28.
Learning Predictive State Representations 2003
Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, and Peter Stone, In Proceedings of the Twentieth International Conference on Machine Learning, August 2003.
TacTex AA Binary The binary version of our 2009 TacTex AA agent, along with many other teams' agents, are available at the ... 2009

TacTex SCM Binaries Binary versions of all TacTex SCM (2005-2008) agents, along with many other teams' agents, are available at the ... 2008

TacTex SCM Starter Agent The purpose of this agent is to serve as a starting point for new participants in the TAC SCM competition. The agent is ... 2006

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