Personalized Game Agent Instantiation via Cloud-Based AI Service
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Solution Overview
Problem
Game development studios face challenges in incorporating advanced AI systems due to the need for custom domain expertise, making it difficult to create personalized AI-controlled agents that can adapt to user preferences across different games.
Innovation Solution
A personalized agent service uses machine learning models to train agents that understand user interactions, preferences, and strategies, allowing these agents to be instantiated across various games without requiring specific game modifications, using computer vision and speech recognition to interpret user inputs and generate appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advanced AI systems are incorporated into games, then agent intelligence and personalization capability are improved, but device complexity and integration difficulty increase
Solution Approach 1:
A cloud-based agent service acts as an intermediary between the game client and AI functionality. The service handles complex machine learning model execution, game state analysis, and agent personalization, while the client only needs to communicate game states and receive agent actions. This mediator approach enables advanced AI capabilities without increasing client device complexity.
Solution Approach 2:
The patent extracts the complex AI processing requirements from the game client device and relocates them to a cloud-based service. The machine learning models, personalization algorithms, and game state analysis are all performed externally, allowing the client to maintain simplicity while still accessing sophisticated AI-powered agents.
2Adaptability or versatility
If game-specific AI integration is implemented, then AI functionality is improved, but ease of manufacture and deployment worsen
Solution Approach 1:
The cloud-based agent service provides a universal platform that can serve multiple games and multiple users simultaneously. The same service infrastructure handles different game titles, different agent types, and different user preferences, eliminating the need for each game to implement its own custom AI integration. This multi-functional approach simplifies deployment across diverse gaming applications.
3Adaptability or versatility
If personalized agents are created for each user, then user experience is improved, but loss of time for training and customization increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with general gaming knowledge and strategies before deploying them to users. When a user first interacts with an agent, the model already possesses foundational capabilities, allowing personalization to occur rapidly through continued interaction rather than requiring extensive initial training periods.
Solution Approach 2:
The system implements continuous feedback loops where the agent learns from user interactions, game outcomes, and user preferences in real-time. This feedback mechanism enables the agent to rapidly adapt and personalize its behavior during actual gameplay sessions, reducing the perceived training time while maintaining high personalization levels.
Data Source
AI summary
Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. A user may request that a personalized agent join the user's gameplay session. The user device sends a request for the personalized agent to a game platform. The game platform determines whether the user has a license to execute a second instance of the game. When the user has a license to execute a second instance of the game, the second instance of the game may be executed on the user device. Information received from a personalized agent service is used to instantiate a personalized agent in the second instance of the game.