Simulation
Agent-oriented software engineering for successful TAC participation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Walverine: a Walrasian trading agent
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Bidding Algorithms for Simultaneous Auctions: A Case Study
Autonomous Agents and Multi-Agent Systems
Bidding under uncertainty: theory and experiments
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Walverine: a Walrasian trading agent
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Mertacor: a successful autonomous trading agent
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)
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
RoxyBot-06: an (SAA)2TAC travel agent
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The supply chain trading agent competition
Electronic Commerce Research and Applications
A long-term profit seeking strategy for continuous double auctions in a trading agent competition
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
The price of independence in simultaneous auctions
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged victorious in the travel division of the 2006 Trading Agent Competition in a photo finish. At a high level, the design of many successful trading agents can be summarized as follows: (i) price prediction: build a model of market prices; and (ii) optimization: solve for an approximately optimal set of bids, given this model. To predict, RoxyBot builds a stochastic model of market prices by simulating simultaneous ascending auctions. To optimize, RoxyBot relies on the sample average approximation method, a stochastic optimization technique.