Applying Artificial Intelligence and Machine Learning Techniques to Create Varying Play Style in Artificial Game Opponents

Artificial Intelligence is quickly becoming an integral part of the modern world, employed in almost every modern industry we interact with. Whether it be self-drive cars, integration with our web clients or the creation of actual intelligent companions such as Xiaoice1, artificial intelligence is now an integrated and critical part of our daily existence. The application of artificial intelligence to games has been explored for several decades, with many agents now competing at a high level in strategic games which prove challenging for human players (e.g. Go and Chess). With artificial intelligence now able to produce strong opponents for many games, we are more concerned with the style of play of artificial agents, rather than simply their strength.

Our work here focusses on the modification of artificial game opponents to create varied playstyle in complex games. We explore several techniques of modifying Monte Carlo Tree Search in an attempt to create different styles of play, thus changing the experience for human opponents playing against them. We also explore improving artificial agent strength, both by investigating parallelization of MCTS and by using Association Rule Mining to predict opponent’s choices, thus improving our ability to play well against them.