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
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
Journal of Artificial Intelligence Research
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
A comparison of multi-agents competing for trading agents competition
WSEAS Transactions on Computers
Comparative analysis of multi-agents competing for trading agents competition
AIC'08 Proceedings of the 8th conference on Applied informatics and communications
RoxyBot-06: stochastic prediction and optimization in TAC travel
Journal of Artificial Intelligence Research
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In this paper, we describe our entrant in the travel division of the 2006 Trading Agent Competition (TAC). At a high level, the design of many successful autonomous 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, we simulate simultaneous ascending auctions. To optimize, we apply the sample average approximation method. Both of these procedures might naturally be abbreviated SAA; hence the title of this paper. Our agent dominated the preliminary and seeding rounds of TAC Travel in 2006, and emerged as champion in the finals in a photo finish.