Distributed AI Training via Player Gameplay Data
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Solution Overview
Problem
Developing rule sets for video games using traditional trial-and-error methods like reinforcement learning requires significant computational resources and time, especially when simulating multiple environments, which can be costly and inefficient.
Innovation Solution
A system and method that utilize players' computing devices to collect game state data during gameplay, applying exploratory rule sets and updating them based on player interactions, thereby reducing the need for simulated environments and conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulators are used to generate simulated environments for training rule sets, then the rule sets can be developed through trial-and-error interactions, but the computational resources and time required become excessive
Solution Approach 1:
The patent uses player gameplay as a copy of simulated environments. Instead of creating virtual simulations, the system captures actual player interactions with the game, which naturally provide the trial-and-error data needed for training rule sets. This approach replaces resource-intensive simulations with leveraging existing player behavior data.
Solution Approach 2:
The system makes players unwittingly participate in the training process by collecting their gameplay data. Players continue playing the game normally while their interactions automatically contribute to training rule sets, eliminating the need for dedicated training simulations and reducing computational resource requirements.
2Reliability
If traditional simulators are used to simulate trial-and-error interactions, then rule sets can be trained through reinforcement learning, but the time required becomes excessive
Solution Approach 1:
The patent replaces time-consuming simulated environments with actual player gameplay sessions. By capturing real player interactions during normal game play, the system obtains training data that would otherwise require extensive simulation time to generate, thereby reducing training time while maintaining quality.
Solution Approach 2:
The system continuously collects gameplay data during ongoing player sessions rather than waiting for dedicated training periods. This continuous data collection during normal game play enables parallel training across multiple players simultaneously, dramatically reducing the total time required to train rule sets.
3Reliability
If a system creates tens, hundreds, or thousands of simulated environments for training, then comprehensive rule set development is achieved, but the cost becomes excessive
Solution Approach 1:
Instead of creating multiple simulated environments, the patent leverages actual player gameplay from multiple users. Each player's gameplay session provides a unique set of interactions that contribute to comprehensive rule set training, achieving the same comprehensiveness as thousands of simulations but with far lower computational cost.
Solution Approach 2:
The system serves dual purposes: players are both enjoying the game and simultaneously contributing to training data collection. This multi-functionality allows the system to achieve comprehensive training data without requiring dedicated simulation infrastructure, reducing computational cost while maintaining training comprehensiveness.
Data Source
AI summary
System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.