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The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effectivein optimising algorithm parameters in Game AI. A potential weakness is the useof a simple average of all component Tuples in the model. This studyinvestigates a refinement to the N-Tuple model used in NTBEA by weighting thesecomponent Tuples by their level of information and specificity of match. Weintroduce weighting functions to the model to obtain Weighted- NTBEA and testthis on four benchmark functions and two game environments. These tests showthat vanilla NTBEA is the most reliable and performant of the algorithmstested. Furthermore we show that given an iteration budget it is better toexecute several independent NTBEA runs, and use part of the budget to find thebest recommendation from these runs.

Weighting NTBEA for Game AI Optimisation