Analyzing Enemy Behavior Difficulty: An Automated Framework Exploiting Behavior Trees

Balancing difficulty is a cornerstone of effective video game design, directly influencing player engagement and satisfaction. Among the various type of difficulty, comprehensive difficulty of in-game enemies poses a unique challenge due to the intricate rules and parameters defining their behavior. This paper addresses the gap in tools for automatically assessing enemy difficulty, particularly during early development stages where machine learning (ML)-based approaches are often impractical. We propose a novel framework for the automatic measurement of the comprehensive difficulty of enemies modeled using Finite-State Machines (FSM) or Behavior Trees (BT). By leveraging the standardized structure of BTs, our approach calculates difficulty based on two key factors: the amount of information, defined by the number of states, and the structural complexity, quantified using cyclomatic complexity. Additionally, we introduce a method for converting FSM models into BTs, ensuring compatibility with our assessment framework. To validate our method, we conducted experiments using enemy models from Super Mario, demonstrating that our automated algorithm produces results consistent with the intended difficulty levels designed by game developers. By reducing reliance on extensive playtesting, this tool empowers developers to iteratively and objectively evaluate enemy difficulty, even in the early stages of development. Our findings underscore the potential of graph-based methods to revolutionize enemy design workflows, paving the way for more efficient and precise balancing in video game development. This framework also highlights the feasibility of integrating such tools into commercial game engines like Unreal Engine 5 to streamline development processes.