Combining pathfmding algorithm with Knowledge-based Monte-Carlo tree search in general video game playing

Abstract

This paper proposes a general video game playing AI that combines a pathfmding algorithm with Knowledge-based Fast-Evolutionary Monte-Carlo tree search (KB Fast-Evo MCTS). This AI is able to acquire knowledge of the game through simulation, select suitable targets on the map using the acquired knowledge, and head to the target in an efficient manner. In addition, improvements have been proposed to handle various features of the GVG-AI platform, including avatar type changes, portals and item usage. Experiments on the GVG-AI Competition framework has shown that our proposed AI can adapt to a wide range of video games, and performs better than the original KB Fast-Evo MCTS controller in 75% of all games tested, with a 64.2% improvement on the percentage of winning.