Past research has been concerned with the potential of embedding deterministic pricing algorithms into {\em pricebots}: software agents used by on-line sellers to automatically price Internet goods.  In this work, probabilistic pricing algorithms based on {\em no-regret\/} learning are explored, in both high-information and low-information settings.  It is shown via simulations that the long-run empirical frequencies of prices in a market of no-regret pricebots can converge to equilibria arbitrarily close to an asymmetric Nash equilibrium; however, instantaneous price distributions need not converge.
['Amy Greenwald', 'Jeffrey O. Kephart']
Probabilistic pricebots