AI Casino Avatars Using Cross-Metaverse Player Models
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Solution Overview
Problem
Existing virtual reality gaming environments lack personalized and intelligent avatars that can adapt to individual player preferences and behaviors, providing tailored interactions and recommendations based on aggregated data across multiple platforms.
Innovation Solution
The system aggregates player information from various sources to train a player model and game model, enabling an artificially intelligent avatar to interact with players, provide strategic advice, and adjust gameplay based on emotional states, using machine learning and biometric feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If player information is aggregated from multiple sources and used to train personalized models, then avatar personalization and adaptability improve, but data privacy risks and system complexity increase
Solution Approach 1:
The system segments player information aggregation by source type (gaming channels, metaverses, third-party platforms) and processes different data categories separately (player preferences, behaviors, biometric information). This modular approach manages complexity while enabling comprehensive personalization.
Solution Approach 2:
A player model acts as an intermediary between raw aggregated data and avatar interactions. The model processes and interprets data from multiple sources, translating diverse information into actionable insights that guide avatar behavior without requiring direct complex processing of all source data.
2Ease of operation
If biometric information is collected and processed to determine emotional states, then player interaction quality improves, but data security and privacy concerns increase
Solution Approach 1:
Biometric data is processed locally at the point of collection within the virtual reality environment rather than being transmitted to remote servers. This localized processing minimizes data exposure during transmission while enabling real-time emotional state detection for improved player interaction.
Solution Approach 2:
The system uses biometric feedback to automatically adjust avatar interactions and provide personalized recommendations without requiring explicit player input. The emotional state detection operates autonomously to optimize the gaming experience while maintaining data security through minimal data retention.
3Productivity
If the avatar provides personalized recommendations and strategic advice based on player data, then player engagement improves, but the risk of influencing player behavior and creating dependency increases
Solution Approach 1:
The avatar provides recommendations based on real-time feedback from player actions and emotional states detected through biometric data. This feedback loop adapts suggestions to player responses, maintaining engagement while allowing players to retain control over their gaming decisions through observable feedback mechanisms.
Solution Approach 2:
The system dynamically adjusts the tone and specificity of avatar recommendations based on detected emotional states and player performance parameters. When players show signs of excessive engagement or dependency, the system modifies its interaction parameters to provide more balanced guidance.
Data Source
AI summary
Embodiments of the present disclosure are directed to artificially intelligent player avatars in a virtual casino. Generally speaking, various types of information related to a player can be collected and aggregated across a number of different real world and virtual reality sources. This aggregated data can be used to generate a player model. Similarly, information can be collected about one or more electronic games and this information can be used to generate a game model for each game. An avatar can then be generated and presented in a virtual reality gaming venue, e.g., a virtual casino. This avatar can then interact with the play based on the player model and the game model.