AI Agent Training via Gameplay Mimicry for Personalized Metaverse Assets
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Solution Overview
Problem
Current AI systems in the metaverse lack the ability to create personalized, trainable assets that accurately replicate specific individuals or groups, limiting user experiences and interactions by relying on generic, preprogrammed non-player characters.
Innovation Solution
A novel AI system utilizing machine learning, natural language processing, and computer vision to train non-fungible assets (bots) through gameplay data, allowing for the creation of personalized, trainable agents that can mimic human behavior and adapt over time, enabling realistic interactions and immersive experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic preprogrammed non-player characters are used, then device complexity is reduced and ease of manufacture is improved, but adaptability and realism of interactions deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing gameplay data, behavioral patterns, and personal attributes of real users before creating the AI agent. This pre-processing of data allows the agent to be pre-configured with personalized characteristics, reducing the complexity of real-time adaptation while maintaining high adaptability to specific user behaviors and preferences.
Solution Approach 2:
The invention creates a digital copy or replica of a real user's behavior patterns, personality traits, and gameplay style by analyzing their existing data. This copying approach enables the AI agent to replicate specific individual characteristics without requiring complex real-time learning mechanisms, thus achieving high adaptability with manageable system complexity.
2Reliability
If personalized trainable assets are created, then adaptability and user experience are improved, but manufacturing complexity and training requirements increase
Solution Approach 1:
The system collects and processes user data in advance during normal gameplay sessions, building up a comprehensive profile before the AI agent needs to be deployed. This preliminary data gathering and processing reduces the manufacturing complexity by preparing all necessary training materials beforehand, while ensuring high accuracy in replicating user behavior.
Solution Approach 2:
The AI agent training system utilizes the user's own gameplay data and behavioral patterns as training material, eliminating the need for external manual programming or configuration. The system automatically extracts relevant features and patterns from the user's existing gameplay, making the creation process easier while maintaining high personalization accuracy.
3Adaptability or versatility
If static normalized bot distributions are used, then manufacturing simplicity is maintained, but adaptability to specific users and dynamic behavior are lost
Solution Approach 1:
The system applies local quality by creating highly personalized AI agents tailored to each specific user's behavior patterns and preferences, rather than using a uniform approach for all users. Each agent is trained on individual user data, providing localized adaptation to specific gaming styles, which increases personalization while the modular training architecture keeps overall complexity manageable.
Solution Approach 2:
The invention dynamically adjusts parameters of the AI agent based on the specific user being replicated, including behavioral parameters, personality traits, and gameplay preferences. This parameter-based personalization approach allows for high adaptability to specific users while maintaining a standardized underlying framework, thus controlling training complexity through systematic parameter adjustment rather than complete reconfiguration.
Data Source
AI summary
The present invention relates to systems and methods for training artificial intelligence assets. The system comprises a digital market module that facilitates the acquisition of a bot from a digital market. A game module enables a user to play a game using the acquired bot. A training module is configured to train the bot through game play against one or more players, employing a mimicry technique. A feedback module obtains training data on the bot's performance in the game, capturing characteristics of the game play, and stores the data in metadata or a database. The system offers an efficient and effective approach to train artificial intelligence assets in the context of gaming, allowing for improved performance and adaptability based on real-time user interactions.