Commodity trading using an agent-based iterated double auction
Proceedings of the third annual conference on Autonomous Agents
High-performance bidding agents for the continuous double auction
Proceedings of the 3rd ACM conference on Electronic Commerce
Agent-oriented software engineering for successful TAC participation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Strategic sequential bidding in auctions using dynamic programming
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A principled study of the design tradeoffs for autonomous trading agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
Rule-Based Specification of Auction Mechanisms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Walverine: a Walrasian trading agent
Decision Support Systems - Special issue: Decision theory and game theory in agent design
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Generating trading agent strategies: analytic and empirical methods for infinite and large games
Generating trading agent strategies: analytic and empirical methods for infinite and large games
A novel method for automatic strategy acquisition in N-player non-zero-sum games
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition (Intelligent Robotics and Autonomous Agents)
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Strategic bidding in continuous double auctions
Artificial Intelligence
Approximate strategic reasoning through hierarchical reduction of large symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Use of Markov chains to design an agent bidding strategy for continuous double auctions
Journal of Artificial Intelligence Research
ATTac-2000: an adaptive autonomous bidding agent
Journal of Artificial Intelligence Research
Evolutionary stability of behavioural types in the continuous double auction
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Three automated stock-trading agents: a comparative study
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Strategy exploration in empirical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Strategic analysis with simulation-based games
Winter Simulation Conference
A grey-box approach to automated mechanism design
Electronic Commerce Research and Applications
Scaling simulation-based game analysis through deviation-preserving reduction
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Expectation of trading agent behaviour in negotiation of electronic marketplace
Web Intelligence and Agent Systems
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We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-theoretic analysis with reinforcement learning. We apply this methodology to the classic Continuous Double Auction game, conducting the most comprehensive CDA strategic study published to date. Empirical game analysis confirms prior findings about the relative performance of known strategies. Reinforcement learning derives new bidding strategies from the empirical equilibrium environment. Iterative application of this approach yields strategies stronger than any other published CDA bidding policy, culminating in a new Nash equilibrium supported exclusively by our learned strategies.