Player Model for Video Game Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Game development faces challenges in quality assurance due to unpredictable user behavior and limitations of standard testing methods, such as fuzz testing and custom AI, which may not cover all game scenarios effectively.
Innovation Solution
A system that generates a computer-implemented player model based on correlations between user inputs and draw signals, using machine learning techniques to simulate user behavior and provide predictive feedback on game quality and success metrics, allowing for simulations of game interactions and adaptations to new game versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard software engineering techniques are used for testing, then the testing process is systematic and structured, but the reliability of testing decreases because users may play the game in unexpected ways that cannot be predicted
Solution Approach 1:
The patent creates player models that are digital copies or representations of actual users, which replicate user behavior patterns through machine learning. These player model copies can then be used to systematically test the game in various scenarios, combining the structure of automated testing with the unpredictability of real user behavior.
Solution Approach 2:
The patent uses parameter changes by training player models on various user behavior parameters and then modifying these parameters to simulate different user types and playstyles. This allows the testing system to explore a wide range of behavioral scenarios while maintaining systematic control over the testing process.
2Reliability
If fuzz testing with random inputs is used, then portions of software not covered by human testers may be tested, but the productivity decreases because the process is time consuming and not predictive about software quality
Solution Approach 1:
The player models are self-learning systems that automatically improve their understanding of user behavior through machine learning algorithms. They autonomously analyze user inputs and draw signals to identify patterns, eliminating the need for manual configuration of test scenarios and significantly improving testing productivity while maintaining comprehensive coverage.
Solution Approach 2:
The system implements feedback loops where player models continuously learn from actual user behavior data and refine their predictions. This feedback mechanism allows the system to become progressively more accurate in predicting user actions and identifying potential issues, making the testing process both efficient and predictive of actual software quality.
3Reliability
If a custom AI is written for the game to test specific scenarios, then coverage of particular game portions improves, but the adaptability decreases because the AI will only find portions of the game it has been coded to find
Solution Approach 1:
The player models are dynamic systems that adapt their behavior based on the game state and learned user patterns. Rather than being static rule-based AI, these models continuously adjust their actions based on real-time feedback from the game environment and their training data, enabling them to cover a wide range of game scenarios without being explicitly programmed for each one.
Solution Approach 2:
The player model framework is designed to be universal and applicable across different game scenarios and types. By training on diverse user behavior data and using general machine learning techniques, a single player model system can adapt to test various portions of the game, eliminating the need to write custom AI for each specific scenario while maintaining comprehensive coverage.
Data Source
AI summary
A player model for a video game is generated based on inputs received from users who have played past versions of the game. The player model can be used to simulate user actions in a new version of the video game and make predictions about average user session length, average earnings per session, number of games played per day, etc. More than one player model may be generated for a game. Each player model for a game may represent one or more features, such as a user skill level, for a group of users.