Evolution is the process of adapting to a potentially dynamic environment. By utilising the implicit learning characteristic of evolution in our algorithms, we can create computer programs that learn, and evolve, in environments containing uncertain (imperfect) information. We propose to use evolutionary algorithms to learn to play games of imperfect information; in particular, the game of poker table . A learning architecture suitable for designing computer opponents is introduced, with experiments on a simplied version of the game used to justify the concept.poker table
