Analyzing Enemy Behavior Difficulty: An Automated Framework Exploiting Behavior Trees
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".