From Explainability to Interpretability: A Case Study on Game AI Using Program Synthesis

Abstract

Many recent advances in artificial intelligence remain difficult to apply in real games due to their black-box nature. These systems often lack transparency and control, making them difficult to integrate into games where player experience and narrative coherence are important. Unpredictable or inexplicable agent behavior can confuse players and frustrate developers. My dissertation explores how program synthesis can address this issue by generating interpretable, controllable representations of agent behavior. Instead of relying on black-box neural network policies, symbolic programs are extracted or generated that capture agent logic in a readable and editable form. Several methods are explored: synthesizing functional programs to imitate and explain game agents, adapting logical program policies to multi-agent settings, and evaluating large language models for code generation across domains such as simplified Atari games, Baba is You, and also tabletop games. An open research question is whether the created conceptual program libraries are transferable across different game domains. My research aims to bridge the gap between AI capabilities and game development needs by making agent behavior transparent, explainable, and adaptable for developers.