Automated Difficulty Assessment Model for Comprehensive Difficulty in Games
Bibliographic record
Abstract
Comprehensive difficulty in a game can be defined as the challenge presented by learning the game's system, encompassing fundamental rules, objectives, and parameters. Achieving a well-balanced difficulty level is crucial for a game's success. In the industry, rules and objectives are commonly designed using a Static Game Balance (SGB) system. This involves human designers handcrafting elements such as game mechanics and the core behavior of enemies. In the early stages of game production, particularly during prototyping without access to player data, it becomes challenging to objectively assess the difficulty of these manually designed elements. Limited research has addressed automatic difficulty assessment in the context of SGB, with most studies focusing on executive difficulty, which pertains to a player's motor skills, such as dexterity. However, the industry is in need of more automated software tools to optimize game production. In this paper, we propose a novel method for automatically measuring the comprehensive difficulty of game enemies. Our approach, owing to its generalizability and the standardized way of defining enemies using state machines, lever-ages the properties of graphs (ex. number of states, transitions, cyclomatic complexity, etc.) to establish a standardized model for comprehensive difficulty assessment in games. We present this model along with the results of an initial exploratory experiment, demonstrating the potential of our approach and its feasibility for integration as a plugin in game engines like Unreal Engine 5.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".