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 …

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 …

Monte Carlo Tree Search is one of the main search methods studied presently. It has
demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan …

Although Reinforcement learning has already been considered one of the most important
and well-known techniques of machine learning, its applicability remains limited in the real …

In fighting games, individual players of the same skill level often exhibit distinct strategies
from one another through their gameplay. Despite this, the majority of AI agents for fighting …

Abstract Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for
agent decision-making, especially when enhanced by Deep Learning (DL), in mastering …

Neuroevolution represents a growing research field in Artificial and Computational
Intelligence. The adjustment of the network weights and the topology is usually based on a …

Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a
wide range of game genres. However, existing research primarily focuses on optimizing …

Numerical weather prediction (NWP) data possess internal inaccuracies, such as low NWP
wind speed corresponding to high actual wind power generation. This study is intended to …

Fighting games represent a challenging problem for computer-controlled characters.
Therefore, they have attracted considerable research interest. This paper investigates novel …