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