Granular Games in Real-Time Environment
Maciej Świechowski, Dominik Ślȩzak · 2018
We propose a new approach to assist computer game creators in introducing AI agent-players into their games. We point out that traditional methods, such as Monte Carlo Tree Search (MCTS), may not provide creators with good interfaces to embed the required AI elements because of too fine-grained space of (often loosely defined) game states. Thus, we suggest to follow the paradigms of information granulation and re-define states/actions at a higher level of abstraction, so the MCTS algorithms can operate on more general concepts, which reflect the creators' domain knowledge. In our approach, the game developers are responsible for specification of mechanisms behind particular high-level states/actions from the perspective of "real world of the game". Meanwhile, the MCTS routines take advantage of the fact that many unique sequences of fine-grained actions become to fall into the same clusters reflecting information granules corresponding to the introduced concepts.