标题： EXPIL：游戏学习中的解释性谓词发明 显示英文标题

标题： EXPIL: Explanatory Predicate Invention for Learning in Games

摘要： 强化学习（RL）已被证明是训练在各种游戏中表现出色的智能体的强大工具。 然而，神经网络模型的黑箱特性常常阻碍我们理解智能体行为背后的推理过程。 最近的研究尝试通过使用预训练神经智能体的指导来编码基于逻辑的策略，从而实现可解释的决策。 此类方法的一个缺点是需要以谓词形式提供大量预先定义的背景知识，这限制了其适用性和可扩展性。 在本工作中，我们提出了一种新方法，游戏中的解释性谓词发明（EXPLANATORY PREDICATE INVENTION FOR LEARNING IN GAMES，EXPIL），该方法从预训练的神经智能体中识别和提取谓词，随后用于基于逻辑的智能体，从而减少对预先定义的背景知识的依赖。 我们在各种游戏中进行的实验评估表明，EXPIL在实现逻辑智能体的可解释行为的同时，所需背景知识更少。 显示英文摘要

摘要： Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel in various games. However, the black-box nature of neural network models often hinders our ability to understand the reasoning behind the agent's actions. Recent research has attempted to address this issue by using the guidance of pretrained neural agents to encode logic-based policies, allowing for interpretable decisions. A drawback of such approaches is the requirement of large amounts of predefined background knowledge in the form of predicates, limiting its applicability and scalability. In this work, we propose a novel approach, Explanatory Predicate Invention for Learning in Games (EXPIL), that identifies and extracts predicates from a pretrained neural agent, later used in the logic-based agents, reducing the dependency on predefined background knowledge. Our experimental evaluation on various games demonstrate the effectiveness of EXPIL in achieving explainable behavior in logic agents while requiring less background knowledge.