KEEPING ADAPTIVE GAME AI INTERESTING

We propose a method for automatically learning diverse but effective macros that can be used as components of game AI scripts. Macros are learnt by a selection-based optimisation method that maximises an interestingness measure. Our demonstrations are performed in a CRPG simulation with two wizards duelling. The results show that the macros learned this way can increase adaptivity, and diversity, while retaining playing strength.