Fast algorithms for finding randomized strategies in game trees
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Sequential Auctions for the Allocation of Resources with Complementarities
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic Automated Bidding in Multiple Auctions
Electronic Commerce Research
Monte Carlo approximation in incomplete information, sequential auction games
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Efficient Bidding Strategies for Simultaneous Cliff-Edge Environments
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Modeling human decision making in cliff-edge environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the rate of convergence of fictitious play
SAGT'10 Proceedings of the Third international conference on Algorithmic game theory
Efficient bidding strategies for Cliff-Edge problems
Autonomous Agents and Multi-Agent Systems
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We model sequential, sealed-bid auctions as a sequential game with imperfect and incomplete information. We develop an agent that, through fictitious play, constructs a policy for the auctions that takes advantage of information learned in the early stages of the game, and is flexible with respect to assumptions about the other bidders' valuations. Because the straightforward expansion of the incomplete information game is intractable, we develop more concise representations that take advantage of the sequential auctions' natural structure. We examine the performance of our agent versus agents that play perfectly, agents that also create policies using Monte-Carlo, and agents that play myopically. The technique performs quite well in these empirical studies, though the tractable problem size is still quite small.