A q-learning based adaptive bidding strategy in combinatorial auctions
Proceedings of the 11th International Conference on Electronic Commerce
An adaptive bidding strategy for combinatorial auction-based resource allocation in dynamic markets
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Combinatorial auctions, where bidders are allowed to put bids on bundles of items, are preferred to single-item auctions in the resource allocation problem because they allow bidders to express complementarities (substitutabilities) among items and therefore achieve better social efficiency. Although many works have been conducted on combinatorial auctions, most of them focus on the winner determination problem and the auction design. A large unexplored area of research in combinatorial auctions is the bidding strategies. In this paper, we propose a new adaptive bidding strategy in multi-round combinatorial auctions in static markets. The bidder adopting this strategy can adjust his profit margin constantly according to bidding histories to maximize his expected utility. Experiment results show that the adaptive bidding strategy performs fairly well when compared to the optimal fixed strategy in different market environments, even without any prior knowledge.