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Hybrid fighting game AI using a genetic algorithm and Monte Carlo tree search

Real-time video game problems are very challenging because of short response times and numerous state space issues. As global companies and research institutes such as Google, Facebook, Intel and Carnegie Mellon university continue to develop Artificial Intelligence(AI) and participated in various Game AI competitions, many AI developers are implementing algorithms, such as Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL), to solve problems. However, these algorithms also have a number of limitations, including the inability to effectively calculate the number of possible cases present in the games and the large amount of time required for computation. We combine the genetic algorithm, which is very effective in finding general solutions to solve the constraints of AI, and the MCTS, which dominates the recent Fighting Game AI Competition, into a hierarchical structure. We propose to call it Hybrid Fighting Game AI.