Automated Game Level Assessment via Player Model Forecasting
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Solution Overview
Problem
Current game distribution and updating mechanisms require frequent installation and compilation of game applications, leading to high bandwidth and resource usage, and lack efficient customization options for different players or player groups, limiting the ability to provide tailored gameplay experiences.
Innovation Solution
A mechanism where a compiled game engine is persistently installed on a client device, with game configuration values, such as game rules and interactive content, provided as non-compiled data for customization, allowing for targeted and automated generation of custom game content based on player models, enabling fine-grained customization without modifying the game engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If frequent installation and compilation of game applications is performed, then customization options for different players are improved, but bandwidth and resource usage increase
Solution Approach 1:
The game application is segmented into a persistent compiled game engine and separate non-compiled configuration data. The engine remains installed locally while only lightweight configuration files are transmitted and updated, eliminating the need for frequent full application downloads and compilations.
Solution Approach 2:
Customization data is extracted from the compiled game application and stored separately as non-compiled configuration data. This allows the game engine to remain stable and installed while customization elements are independently updated and transmitted with minimal bandwidth consumption.
2Manufacturing precision
If manual creation and testing of custom game levels is performed, then content quality is improved, but labor costs and time consumption increase
Solution Approach 1:
The system employs automated assessment engines that self-evaluate generated game levels against predefined quality criteria. The engine autonomously tests whether levels meet difficulty targets, progression requirements, and other quality metrics without requiring manual human review for each level.
Solution Approach 2:
An automated feedback loop is established where the assessment engine evaluates generated levels, provides quality metrics, and enables iterative refinement. This continuous feedback mechanism ensures content quality standards are met while eliminating manual testing bottlenecks.
3Manufacturing precision
If automated assessment of generated levels is implemented, then content quality control is improved, but system complexity increases
Solution Approach 1:
A dedicated automated assessment engine serves as an intermediary component between the level generation system and the final game content. This specialized module handles the complex evaluation logic, keeping the main game engine simple while enabling sophisticated quality control through the intermediary assessment layer.
Data Source
AI summary
A game server accesses player model representing subset of players. Player model is generated based on previous in-game behavior of the subset of players while playing a computer-implemented game. The game server accesses a set of interactive content items associated with the game. The game server forecasts, using the player model, a sequence of user actions of the subset of players during gameplay of the game. The sequence of user actions represents a prediction of user interaction with the set of interactive content items. The game server computes, based on the forecasted sequence of user actions and software-defined outcomes of the forecasted sequence of user actions in the game, configuration values for the set of interactive content items. The game server causes execution of gameplay at a client device associated with the player model. The gameplay is according to the computed configuration values for the set of interactive content items.