Large language models (LLMs) have shown success in handling simple games with imperfect information and enabling multi-agent coordination, but their ability to facilitate practical collaboration against other agents in complex, imperfect information environments, especially in a non-English environment, still needs to be explored. This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games requiring agent collaboration under imperfect information, comparing their performance to established baselines using other types of agents. We propose a Theory of Mind (ToM) planning technique that allows LLM agents to adapt their strategy against various adversaries using only game rules, current state, and historical context as input. An external tool was incorporated to mitigate the challenge of dynamic and extensive action spaces in this card game. Our results show that although a performance gap exists between current LLMs and state-of-the-art reinforcement learning (RL) models, LLMs demonstrate ToM capabilities in this game setting. It consistently improves their performance against opposing agents, suggesting their ability to understand the actions of allies and adversaries and establish collaboration with allies. To encourage further research and understanding, we have made our codebase openly accessible.

Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research. Much of the research is concerned with game-playing agents and their decision making processes. The game of Guandan (literally, "throwing eggs") is a challenging game where even professional human players struggle to make the right decision at times. In this paper we propose a framework named GuanZero for AI agents to master this game using Monte-Carlo methods and deep neural networks. The main contribution of this paper is about regulating agents' behavior through a carefully designed neural network encoding scheme. We then demonstrate the effectiveness of the proposed framework by comparing it with state-of-the-art approaches.

Games have benchmarked AI methods since of a single game, discovering a few new variations on the inception of the field, with classic board games such existing research topics. The set of · Deckbuilding · Gameplaying · Player Modeling AI problems associated with video games has in recent decades expanded from simply playing games to win, to playing games in particular styles, generating game content, 1 Introduction modeling players etc. Different games pose very different challenges for AI systems, and several different For decades classic board games such as Chess, Checkers, AI challenges can typically be posed by the same and Go have dominated the landscape of AI and game. In this article we analyze the popular collectible games research. Often called the "drosophila of AI" in card game Hearthstone (Blizzard 2014) and describe reference to the drosophila fly's significance in biological a varied set of interesting AI challenges posed by this research, Chess in particular has been the subject game. Collectible card games are relatively understudied of hundreds of academic papers and decades of research in the AI community, despite their popularity and [18]. At the core of many of these approaches is designing the interesting challenges they pose. Analyzing a single algorithms to beat top human players. However, game in-depth in the manner we do here allows us to despite IBM's Deep Blue defeating Garry Kasparov in see the entire field of AI and Games through the lens the 1997 World Chess Championships and DeepMind's AlphaGo defeating Lee Sedol in the 2016 Google Deep-Mind Challenge Match [47], such programs have yet While there is value in designing algorithms to win (e.g.