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

TL;DR

This paper introduces playstyle as a new perspective for analyzing AI decision-making in video games, emphasizing the influence of beliefs and values, and proposes methods to measure, generate, and apply playstyle in AI systems.

Contribution

It formalizes the concept of playstyle in AI, develops metrics for its measurement, and explores techniques for generating and applying stylistic behaviors in game AI.

Findings

Proposed a general playstyle metric based on state spaces.

Developed methods for training AI with specific stylistic tendencies.

Analyzed applications in game design and interactive entertainment.

Abstract

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).