Adaptive Game Assistance via Neural Network Player Modeling
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Solution Overview
Problem
The increasing complexity of video games poses challenges for players and developers, as the varied progression paths and lack of adaptive assistance make it difficult for players to navigate and for developers to create effective gaming experiences, especially with the rise of open-ended games that offer extensive player freedom.
Innovation Solution
A video game system that utilizes a deep learning neural network to monitor player interactions, determine experience levels, and provide personalized assistance and adaptive game contexts, including virtual assistants and agents, to enhance player engagement and skill development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video games provide extensive player freedom and open-ended progression, then player engagement and versatility are improved, but player confusion and difficulty of operation increase
Solution Approach 1:
The game system dynamically adjusts the level of guidance and structure based on real-time analysis of player behavior and experience level. The progression system transitions from rigid predefined paths to flexible open-ended exploration as players gain experience, while adaptive assistance mechanisms provide contextual hints and guidance when players struggle, thereby maintaining both player freedom and ease of operation.
Solution Approach 2:
The system continuously monitors player interactions, performance metrics, and progression patterns to provide real-time feedback about player experience level and confusion state. This feedback loop enables the game to adapt its difficulty, guidance level, and progression structure dynamically, ensuring players receive appropriate support while maintaining freedom to explore and choose their own paths.
2Adaptability or versatility
If video games offer varied progression paths and open-ended gameplay, then player engagement is improved, but developer ability to anticipate and provide appropriate assistance deteriorates
Solution Approach 1:
The game system automatically analyzes player behavior patterns, progression data, and performance metrics to generate adaptive assistance content without requiring extensive pre-programming for every possible scenario. The system serves itself by using machine learning algorithms to understand player needs and generate appropriate guidance, hints, and progression adjustments dynamically, thereby reducing the developer burden while maintaining progression variety.
Solution Approach 2:
The system changes multiple game parameters simultaneously based on player state analysis, including difficulty level, guidance intensity, progression rate, and assistance type. By dynamically adjusting these parameters rather than creating fixed progression paths for every scenario, developers can offer varied progression experiences while the system handles the complexity of anticipating and responding to diverse player needs.
3Adaptability or versatility
If video game code provides assistance for multiple possible scenarios, then adaptability is improved, but code complexity increases
Solution Approach 1:
The patent replaces traditional mechanical if-then programming logic with machine learning-based predictive models that analyze player behavior patterns and predict assistance needs. Instead of hardcoding assistance for every possible scenario, the system uses trained models to generate appropriate assistance dynamically based on player state, thereby maintaining high adaptability while significantly reducing code complexity and improving maintainability.
Data Source
AI summary
A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.