Google llc (20240198232). EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE simplified abstract
Contents
EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE
Organization Name
Inventor(s)
Nathan Sun Martz of San Francisco CA (US)
Horacio Hernan Moraldo of Mountain View CA (US)
Stewart Miles of San Francisco CA (US)
Leopold Haller of Berkeley CA (US)
Hinako Sakazaki of Albany CA (US)
EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240198232 titled 'EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE
The abstract describes a system and method for training a locally executed actor component in a gaming application using gameplay data models generated by a remote learning service. The actor component learns from observational data based on inferences from the gameplay data model and modifies its actions accordingly.
- Locally executed actor component trained to perform real-time gameplay actions in a gaming application.
- Gameplay data models provided by a remote learning service from server computing systems to the client device.
- Observational data generated by the actor component based on in-game results to improve future actions.
- Remote learning service modifies the gameplay data model based on observational data for continuous improvement.
- Integration of artificial intelligence to enhance gameplay experience and performance.
Potential Applications: - Enhancing gameplay experiences in real-time gaming applications. - Improving the efficiency and effectiveness of AI-controlled characters in games. - Personalized training and learning for gaming applications based on individual gameplay data.
Problems Solved: - Enhancing the adaptability and learning capabilities of AI-controlled characters in gaming applications. - Providing a more immersive and challenging gaming experience for players. - Streamlining the process of training AI components in gaming applications.
Benefits: - Improved gameplay performance and adaptability. - Enhanced user experience through more realistic and challenging gameplay. - Continuous learning and improvement of AI-controlled characters in gaming applications.
Commercial Applications: "AI-Driven Real-Time Gameplay Training System for Gaming Applications" This technology can be utilized by game developers to enhance the AI capabilities of characters in their games, leading to more engaging and immersive gameplay experiences. It can also be integrated into training simulations for various industries to improve the efficiency and effectiveness of AI-controlled entities.
Questions about AI: 1. How does the system ensure the accuracy and relevance of the observational data used for training the actor component? 2. What measures are in place to prevent bias or inaccuracies in the modifications made to the gameplay data model based on observational data?
Original Abstract Submitted
systems and methods are described for training a locally executed actor component to execute real-time gameplay actions in a gaming application based on one or more gameplay data models generated by a remote learning service. a gameplay data model for the gaming application is provided from one or more server computing systems executing the remote learning service to the client computing device. observational data is generated by the local actor component based on in-game results of artificial gameplay actions performed by the local actor component, based at least in part on inferences generated by the actor component using the provided gameplay data model. based on the received observational data, the remote learning service modifies the gameplay data model and provides the modified gameplay data model to the local actor component to improve future artificial gameplay actions.
