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As computational agents are developed for increasingly complicated e-commerceapplications, the complexity of the decisions they face demands advances inartificial intelligence techniques. For example, an agent representing a sellerin an auction should try to maximize the seller's profit by reasoning about avariety of possibly uncertain pieces of information, such as the maximum pricesvarious buyers might be willing to pay, the possible prices being offered bycompeting sellers, the rules by which the auction operates, the dynamic arrivaland matching of offers to buy and sell, and so on. A naive application ofmultiagent reasoning techniques would require the seller's agent to explicitlymodel all of the other agents through an extended time horizon, rendering theproblem intractable for many realistically-sized problems. We have insteaddevised a new strategy that an agent can use to determine its bid price basedon a more tractable Markov chain model of the auction process. We haveexperimentally identified the conditions under which our new strategy workswell, as well as how well it works in comparison to the optimal performance theagent could have achieved had it known the future. Our results show that ournew strategy in general performs well, outperforming other tractable heuristicstrategies in a majority of experiments, and is particularly effective in a'seller?s market', where many buy offers are available.

Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions