It is now well known that decentralised optimisation can be formulated as a
potential game, and game-theoretical learning algorithms can be used to find an
optimum. One of the most common learning techniques in game theory is
fictitious play. However fictitious play is founded on an implicit assumption
that opponents' strategies are stationary. We present a novel variation of
fictitious play that allows the use of a more realistic model of opponent
strategy.