AI Gaming Bots Automate Game Balance Testing
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Solution Overview
Problem
Game development faces challenges in efficiently testing and balancing gaming applications due to the vast number of possible deck combinations and match scenarios in multiplayer games, requiring extensive human labor and time to ensure game balance and player engagement.
Innovation Solution
The game development platform employs AI-based gaming bots and procedural content generation tools to automate testing, balance gameplay, and generate new content, utilizing machine learning algorithms to simulate player behaviors and preferences, thereby reducing human labor and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human testers manually test and balance game content, then testing accuracy and game balance quality are maintained, but testing time and labor costs increase significantly
Solution Approach 1:
The patent creates virtual copies of players (gaming bots) that replicate human player behaviors, preferences, and play styles. These bots copy human testing actions to evaluate game content, enabling automated testing that maintains quality while reducing time and labor requirements
Solution Approach 2:
The patent replaces the mechanical system of manual human testing with an automated electronic system using machine learning algorithms. The system substitutes human testers with computational models that can evaluate game content automatically, eliminating the time-consuming manual process while preserving evaluation accuracy
2Manufacturing precision
If the number of deck combinations and match scenarios is increased to improve game balance, then game balance quality improves, but testing complexity and required resources increase
Solution Approach 1:
The patent implements a self-service testing system where gaming bots autonomously evaluate game content without requiring complex human-coordinated testing infrastructure. The bots independently execute test scenarios, track performance metrics, and provide feedback, simplifying the overall testing complexity while comprehensively evaluating numerous deck and match combinations
Solution Approach 2:
The patent creates a universal testing platform that handles multiple game modes, deck combinations, and match scenarios through a single automated system. The gaming bots are designed to perform various testing functions across different game conditions, reducing the need for separate testing systems for each scenario and thereby reducing overall complexity
3Adaptability or versatility
If more game content is generated to enhance player engagement, then player engagement and customer lifetime value improve, but content development time and resources increase
Solution Approach 1:
The patent performs preliminary evaluation of game content using gaming bots before actual player release. By pre-testing and pre-evaluating content quality, the system identifies promising content that is likely to engage players, allowing developers to prioritize and refine only the most promising content rather than extensively developing all possible content variations
Solution Approach 2:
The patent implements a feedback loop where gaming bots evaluate game content and provide performance data back to developers. This feedback mechanism enables iterative content improvement, where content is generated, evaluated, refined based on bot feedback, and re-tested, efficiently identifying content that maximizes player engagement without requiring extensive manual development of all content variations
Data Source
AI summary
A procedural content generation tool operates by: generating, via image analysis, graphs of existing game content; generating a symmetrical Markov random field (SMRF) model based on the graphs; and automatically generating, via iterative artificial intelligence (AI), new game content based on the SMRF model.