Breaking down the challenge: A comprehensive framework for evaluating enemy difficulty with automated behavior tree analytics

Balancing enemy difficulty is a key aspect of game design, as it directly affects player experience. Evaluating the intrinsic difficulty of enemy behaviors remains challenging, particularly during early development stages when machine learning solutions are impractical. This article introduces a framework for automating enemy difficulty analysis, focusing on behaviors modeled using Behavior Trees and Finite State Machines. The proposed method quantifies difficulty by analyzing graph complexity through the number of behavioral nodes and by assessing structural complexity using cyclomatic complexity. To ensure methodological consistency, we adopt a formalized process for converting Finite State Machines into equivalent Behavior Trees. The approach is validated through experiments involving enemies from games such as Super Mario Bros , Mega Man , Super Punch-Out!! , and Sekiro: Shadows Die Twice , demonstrating a strong correlation between automated difficulty assessments and human expectations. In addition, we developed a prototype tool for Unreal Engine that enables real-time difficulty diagnostics during level design. This integration allows designers to make informed adjustments while reducing reliance on extensive playtesting. Overall, our contributions establish a foundation for graph-based difficulty modeling and outline future research directions in adaptive difficulty, procedural content generation, and AI behavior tuning. The proposed framework and its accompanying Unreal Engine 5 tool enable designers to estimate and compare enemy behavioral difficulty both during early prototyping, when playtesting data are unavailable, and later in production, when AI behaviors are refined and balanced. This dual applicability supports the quantification and adjustment of behavioral complexity throughout the iterative game development process. • An approach using metrics for measuring comprehensive difficulty of enemies based on the structural complexity of graphs. • A case study on enemies in Super Mario Bros., Super Punch Out!!, Mega Man, and Sekiro: Shadow Die Twice using our approach. • A tool in Unreal Engine that estimates the comprehensive difficulty based on its behavior tree.