eMediator: a next generation electronic commerce server
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Autonomous Bidding Agents in the Trading Agent Competition
IEEE Internet Computing
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
The First International Trading Agent Competition: Autonomous Bidding Agents
Electronic Commerce Research
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
Journal of Artificial Intelligence Research
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Bidding for multiple items or bundles on online auctions raises challenging problems. We assume that an agent has a valuation function that returns its valuation for an arbitrary bundle. In the real world all or most of the items of interest to an agent is not present in a single combinatorial auction. We study the problem of bidding for multiple items in a set of simultaneous auctions, each of which sell only a single unit of a particular item. Hence an agent has to bid in multiple auctions to obtain preferred item bundles. While an optimal bidding strategy is known when bidding in sequential auctions, only suboptimal strategies are available when bidding for items sold in auctions running simultaneously. To decide on an agent's bid for simultaneous auctions, we investigate a multi-dimensional bid improvement strategy, which is optimal given an infinite number of restarts. We provide a comparison of this algorithm with existing ones, both in terms of utilities generated and computation time, along with a discussion of the strengths and weaknesses of these strategies.