In-Game AI Assistant for Team Coordination and Player Support
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Solution Overview
Problem
Video game platforms lack adequate assistance for human players to overcome challenging modern games, with no effective solutions to provide sufficient aid during gameplay.
Innovation Solution
Implementing a generative artificial intelligence (AI) assistant using a large language model (LLM) to provide audible and visual outputs, monitor gameplay events, and execute tasks such as health reporting, enemy location, and strategy suggestions, acting as a fifth player in team gameplay or a second player in single-player scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video game complexity is increased to provide more challenging gameplay, then gaming experience is improved, but player ability to overcome difficulties deteriorates
Solution Approach 1:
An AI assistant acts as an intermediary between the player and the complex game system. The assistant monitors game state, interprets player needs, and provides contextualized assistance such as strategy suggestions, resource management advice, and task prioritization, enabling players to navigate complex gameplay without directly simplifying the game itself
Solution Approach 2:
The system enables players to self-diagnose their gameplay difficulties and self-correct through AI-guided decision-making. The AI provides information and options, but players maintain agency to make their own decisions, fostering independent problem-solving while overcoming game challenges
2Productivity
If AI assistant provides comprehensive real-time assistance, then player performance is improved, but player immersion deteriorates
Solution Approach 1:
The AI assistant provides differentiated assistance based on specific game contexts and player needs rather than uniform help. It adjusts the level, type, and timing of assistance dynamically, offering detailed guidance only when necessary and remaining subtle otherwise, preserving immersion in appropriate situations while maintaining performance support
Solution Approach 2:
The assistance system dynamically adapts its behavior based on game state, player skill level, and situational context. It transitions between providing active guidance and passive information availability, adjusting its presence and intervention level to maintain immersion while supporting performance as conditions change
3Measurement precision
If AI assistant monitors multiple gameplay parameters, then task accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The monitoring system is segmented into specialized sub-systems that track different gameplay parameters (health status, resource levels, enemy positions, task progress) independently. Each sub-system focuses on specific metrics and processes them through dedicated algorithms, improving accuracy while managing complexity through modular organization
Data Source
AI summary
Artificial intelligence (AI) models are disclosed to provide in-game AI assistants that can execute in-game tasks for human players in conformance with audible prompts from the human players during multi-player gameplay instances. The AI assistants can therefore report in-game events to help the human players and even act as additional players within the video game, controlling their own video game characters to establish virtual teammates to help the human players advance within the video game.