标题： 游戏风格与人工智能：通过视频游戏视角的初步蓝图 显示英文标题

标题： Playstyle and Artificial Intelligence: An Initial Blueprint Through the Lens of Video Games

摘要： 当代人工智能（AI）的发展主要集中在理性决策上，因其可测量性和适合客观评估而受到重视。 然而在现实情境中，智能代理的决策不仅受逻辑影响，还受到更深层次因素如信念、价值观和偏好的影响。 人类决策风格的多样性源于这些差异，这表明“风格”是智能的一个重要但常被忽视的维度。 本论文引入了“游戏风格”作为观察和分析智能代理决策行为的另一种视角，并从哲学角度探讨其基本含义和历史背景。 通过分析信念和价值观如何驱动意图和行动，我们构建了一个两层的风格形成框架：与环境的外部互动循环和内部的思辨认知循环。 在此基础上，我们形式化了与风格相关的特征，并提出了可测量的指标，如风格容量、风格流行度和演化动态。 研究聚焦于三个核心研究方向：(1) 定义和测量游戏风格，提出基于离散状态空间的一般游戏风格度量，并扩展以量化战略多样性和竞争平衡；(2) 表达和生成游戏风格，探索如何利用强化学习和模仿学习来训练表现出特定风格倾向的代理，并引入一种新颖的人类风格学习和建模方法；(3) 实践应用，分析这些技术在游戏设计和互动娱乐等领域的潜力。 最后，论文概述了未来的研究方向，包括风格在构建人工通用智能（AGI）中的核心作用。 显示英文摘要

摘要： Contemporary artificial intelligence (AI) development largely centers on rational decision-making, valued for its measurability and suitability for objective evaluation. Yet in real-world contexts, an intelligent agent's decisions are shaped not only by logic but also by deeper influences such as beliefs, values, and preferences. The diversity of human decision-making styles emerges from these differences, highlighting that "style" is an essential but often overlooked dimension of intelligence. This dissertation introduces playstyle as an alternative lens for observing and analyzing the decision-making behavior of intelligent agents, and examines its foundational meaning and historical context from a philosophical perspective. By analyzing how beliefs and values drive intentions and actions, we construct a two-tier framework for style formation: the external interaction loop with the environment and the internal cognitive loop of deliberation. On this basis, we formalize style-related characteristics and propose measurable indicators such as style capacity, style popularity, and evolutionary dynamics. The study focuses on three core research directions: (1) Defining and measuring playstyle, proposing a general playstyle metric based on discretized state spaces, and extending it to quantify strategic diversity and competitive balance; (2) Expressing and generating playstyle, exploring how reinforcement learning and imitation learning can be used to train agents exhibiting specific stylistic tendencies, and introducing a novel approach for human-like style learning and modeling; and (3) Practical applications, analyzing the potential of these techniques in domains such as game design and interactive entertainment. Finally, the dissertation outlines future extensions, including the role of style as a core element in building artificial general intelligence (AGI).