Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

With the breakthrough of AlphaGo, human-computer gaming AI has ushered in a big
explosion, attracting more and more researchers all over the world. As a recognized …

Reinforcement learning (RL) combined with deep neural networks has performed
remarkably well in many genres of games recently. It has surpassed human-level …

The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for two-
player video game artificial intelligence. The challenge arises from the large action space …

In this paper, we propose a Monte-Carlo Tree Search (MCTS) fighting game AI capable of
dynamic difficulty adjustment while maintaining believable behaviors. This work targets …

Reinforcement learning has recently been recognized as a promising means of machine
learning, but its applicability remains limited in real-time environment due to its short …

In this paper, we propose a method for implementing a game AI with a persona using Monte-
Carlo Tree Search (MCTS). Video games are now a powerful entertainment media not just …

Fighting games are complex environments where challenging action-selection problems
arise, mainly due to a diversity of opponents and possible actions. In this paper, we present …

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 …