The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
eMediator: a next generation electronic commerce server
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Sequential Auctions for the Allocation of Resources with Complementarities
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
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
Multi-dimensional bid improvement algorithm for simultaneous auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Communications
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Bidding for multiple items or bundles on online auctions raise 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 focus on bidding for multiple items in a set of auctions, each of which sell only a single unit of a particular item. Hence an agent has to bid in multiple auctions to obtain 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. We investigate a hill-climbing bidding strategy, which is optimal given an infinite number of restarts, to decide on an agent's bid for simultaneous auctions. 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.