Automated Difficulty Assessment Model for Comprehensive Difficulty in Games

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.