Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Self-play reinforcement learning, where agents learn by playing with themselves, has been
successfully applied in many game scenarios. However, the training procedure for self-play …

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 …

Online search in games has been a core interest of artificial intelligence. Search in imperfect
information games (eg, Poker, Bridge, Skat) is particularly challenging due to the …

Algorithms with several paradigms (such as rule-based methods, game theory and
reinforcement learning) have achieved great success in solving imperfect information games …

Abstract Self-play Monte Carlo Tree Search (MCTS) has been successful in many perfect-
information twoplayer games. Although these methods have been extended to imperfect …

Parliamentary democracy involves a never-ending cycle of elections, government
formations, and the need for governments to survive in potentially hostile environments …

Abstract Counterfactual Regret Minimization algorithms are the most popular way of
estimating the Nash Equilibrium in imperfect-information zero-sum games. In particular …

Monte Carlo Tree Search (MCTS) is an AI technique that has been successfully applied to
many deterministic games of perfect information, leading to large advances in a number of …

In many card and board games, players cannot see the whole game state, with different
players seeing different parts of the state. In such games, gathering of information …