A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is
Adaptive techniques tend to converge to a single optimum. For adaptive game AI, such convergence is often undesirable, as repetitive game AI is considered to be uninteresting for players. In this paper, we propose a method for automatically learning diverse but effective macros that can be used as components of adaptive game AI scripts. Macros are learned by a cross-entropy method. This is a selection-based optimization method that, in our experiments, maximizes an interestingness measure. Wedoi:10.1109/tciaig.2009.2018706 fatcat:tnlx76dn3zflbalohws5gmvnvu