Intelligent agents for electronic commerce are a popular research topic. There are shopping agents that collect price information for users [Doorenbos, Etzioni, & Weld1997], and information filtering agents that collect interesting publications [Bollacker, Lawrence, & Giles1998]. We are interested in designing agents for online auctions, where buyers and sellers interact with each other. Such agents can work on the behalf of users since users usually do not have time or inclination to monitor the activities in an auction. Sometimes an optimal bidding strategy may be computationally intensive, in which case it is especially useful to have a software agent carry out the bidding. The interesting research issue for us is how an agent takes advantage of the information available and achieves maximal profit in the transactions. This usually refers to how an agent uses past observations to make predictions and choose its optimal bids. We also address design issues such as how an agent works in a web environment and how it gathers information and makes decisions in real time.
We have designed an agent server that works on a user's behalf to submit bids to one of the online auctions--the Michigan AuctionBot. The users specify the names of the auctions they want to participate in, the initial amounts of the goods, and the bidding strategies they prefer. The agent then starts bidding on the AuctionBot for the users. The agents keep bidding until the auction closes, and then report the results back to the users.
Our experiments suggest that an agent's performance in the auction depends not only on its bidding strategy, but also on the bidding strategies of others. A greedy bidding strategy may help the agent to gain in the short run, but may also cause it to lose in the long run.