Video Game State Prediction for Automated Error Detection
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Solution Overview
Problem
Manual error detection in video games is a labor-intensive process that can lead to prolonged development times and poor game quality due to undetected bugs and glitches, affecting functionality and user experience.
Innovation Solution
A data processing apparatus utilizing a trained machine learning model to predict video game images based on input frames and actions, enabling automated error detection by comparing predicted and actual game states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assurance testing is used to detect errors in video games, then error detection can be performed, but the process is labor-intensive and results in prolonged development times
Solution Approach 1:
The patent replaces manual mechanical testing with an automated machine learning system. The ML model automatically analyzes video game states, compares actual game states with predicted states, and detects errors without human intervention, thereby reducing both labor intensity and development time while maintaining error detection capability
Solution Approach 2:
The system enables the video game to self-diagnose errors through automated ML-based analysis. The machine learning model independently evaluates game states, identifies discrepancies between expected and actual behavior, and flags errors without requiring external manual testing, thus accelerating the development process
2Reliability
If manual error detection is used, then errors can be identified, but the process requires significant manual effort and time
Solution Approach 1:
The patent substitutes manual inspection operations with automated machine learning analysis. The ML model automatically processes video game data, compares actual states with predicted states, and detects errors, eliminating the need for manual effort while preserving detection accuracy
Solution Approach 2:
The system creates a predicted copy of the expected game state and compares it with the actual game state. This copying approach allows automated comparison and error detection without manual intervention, maintaining accuracy while reducing operational effort
3Reliability
If comprehensive quality assurance testing is performed to ensure game quality, then error detection improves, but development time increases
Solution Approach 1:
The patent replaces time-consuming manual quality assurance processes with automated machine learning analysis. The ML model rapidly evaluates game states and detects errors, improving game quality assurance while simultaneously increasing development efficiency by reducing the time required for testing
Solution Approach 2:
The machine learning-based error detection system operates continuously and automatically throughout the development process, providing ongoing quality assurance without interrupting the development workflow. This continuous automated analysis ensures high game quality while maintaining productive development pace
Data Source
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AI summary
A data processing apparatus for detecting one or more errors for one or more video images for a video game, the data processing apparatus comprising receiving circuitry to receive a sequence of video images for the video game and one or more action inputs associated with the sequence of video images, prediction circuitry to generate a predicted video image in dependence on at least one video image of the sequence of video images and an action input associated with the at least one video image, the prediction circuitry comprising a trained machine learning model to generate the predicted video image, and error detection circuitry to detect, for one or more video images subsequent to the at least one video image, one or more errors in dependence on the predicted video image.