AI Model Training via Distributed Game Server Data Collection
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Solution Overview
Problem
The complex and time-consuming process of collecting and generating vast amounts of data required to train artificial intelligence (AI) models, such as recognizing objects, is inefficient, as it involves numerous images from various perspectives, making the training process ongoing and labor-intensive.
Innovation Solution
Utilizing a network of servers configured to execute gaming applications, where training data is collected from multiple game plays and used to train an AI model through a deep learning engine, reducing the time and effort needed to build input sets of training data by centralizing the data collection across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vast amounts of training data are collected manually from multiple perspectives, then the AI model can recognize objects accurately, but the data collection process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system uses the gaming application itself to generate training data automatically. The game environment, objects, and scenarios are created and populated by the game's own rendering engine and game logic, eliminating the need for manual data collection. The AI model trains on game states, object interactions, and gameplay scenarios that are naturally generated during normal game operation.
Solution Approach 2:
Instead of manually capturing real-world objects from multiple perspectives, the system creates digital copies of objects within the game environment. These virtual objects are rendered from various angles and conditions automatically by the game engine, providing diverse training data without manual intervention. The game world serves as a virtual laboratory for generating unlimited training scenarios.
2Adaptability or versatility
If comprehensive training data covering all object types and perspectives is collected, then the AI model becomes versatile, but the training process becomes ongoing and never complete
Solution Approach 1:
The system enables continuous training by leveraging the ongoing gameplay data stream. As players interact with the game world, new scenarios, objects, and interactions are continuously generated and fed to the AI model for training. This creates an unending supply of training data that allows the model to continuously improve without requiring periodic manual data collection campaigns.
Solution Approach 2:
The training system is designed to be dynamic and adaptive, adjusting to new game content, objects, and scenarios as they are introduced. The AI model can learn new object types and interactions on-the-fly as they appear in gameplay, rather than requiring pre-planned comprehensive data collection for every possible scenario.
3Ease of manufacture
If training data is collected centrally from a single location, then data management is simplified, but the quantity and diversity of training data are limited
Solution Approach 1:
The system divides the training data generation process across multiple distributed game servers and player devices. Each server and device generates its own training data from local gameplay, creating a distributed network of data generation nodes. This segmentation allows exponential scaling of data volume while maintaining manageable data collection at each individual node through automatic local generation.
Data Source
AI summary
A method for processing an artificial intelligence (AI) model for a gaming application. The method includes training the AI model from a plurality of game plays of a scenario of the gaming application using training state data collected from the plurality of game plays of the scenario and associated success criteria of each of the plurality of game plays. The method includes receiving first input state data during a first game play of the scenario. The method includes applying the first input state data to the AI model to generate an output indicating a degree of success for the scenario for the first game play. The method includes performing an analysis of the output based on a predefined objective. The method includes performing an action to achieve the predefined objective based on the output that is analyzed.