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

Balancing enemy difficulty is essential in game design, significantly affecting player experience. Evaluating the intrinsic difficulty of enemy behaviors can be challenging, especially in early development when machine learning solutions are impractical. This article introduces a framework for automating enemy difficulty analysis, focusing on behaviors modeled with Behavior Trees or Finite-State Machines. The method quantifies difficulty by assessing graph complexity through the number of behavioral nodes and structural complexity using cyclomatic complexity. To ensure compatibility, we used a formalized process for converting Finite-State Machines into equivalent Behavior Trees. Our approach is validated through experiments with enemies from games like Super Mario Bros., Megaman, Super Punch Out!!, and Sekiro: Shadows Die Twice, showing a correlation between automated assessments and human expectations. Additionally, we developed a prototype tool for Unreal Engine, enabling real-time difficulty diagnostics during level design. This integration allows developers to make informed adjustments with less reliance on extensive playtesting. Our contributions lay the groundwork for graph-based difficulty modeling and suggest future developments in adaptive difficulty, procedural generation, and artificial intelligence behavior tuning.