Adaptive Intelligence for Turn-based Strategy Games

Computer games are an increasingly popular form of entertainment. Typically, the quality of AI opponents in computer games leaves a lot to be desired, which poses many attractive challenges for AI researchers. In this respect, Turn-based Strategy (TBS) games are of particular interest. These games are focussed on high-level decision making, rather than low-level behavioural actions. Moreover, they allow the players sufficient time to consider their moves. For efficiently designing a TBS AI, in this paper we propose a game AI architecture named ADAPTA (Allocation and Decomposition Architecture for Performing Tactical AI). It is based on task decomposition using asset allocation, and promotes the use of machine learning techniques. In our research we concentrated on one of the subtasks for the ADAPTA architecture, namely the Extermination module, which is responsible for combat behaviour. Our experiments show that ADAPTA can successfully learn to outperform static opponents. It is also capable of generating AIs which defeat a variety of static tactics simultaneously.