Adaptive Audio Feedback for Accessible Video Lottery Terminals
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Solution Overview
Problem
Conventional gaming systems fail to accommodate players with impairments or disabilities effectively, as existing player behavior models are hard-coded and lack self-evolving AI capabilities, leading to suboptimal gaming experiences and computational challenges in creating tailored models.
Innovation Solution
A Reinforcement Learning (RL)-based system that uses audio output to predict and modify game states for visually impaired players, optimizing audio feedback to enhance gameplay understanding and incorporating haptic feedback, enabling self-evolving AI models that adapt to individual impairments with minimal computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional hard-coded player behavior models are used, then device complexity is reduced, but adaptability to players with impairments deteriorates
Solution Approach 1:
The system employs self-evolving AI models that automatically adapt to individual player needs without requiring manual configuration or complex pre-programming for each impairment type. The reinforcement learning algorithm continuously learns from player interactions and autonomously optimizes audio output modifications, allowing the system to serve itself in adapting to diverse player requirements.
Solution Approach 2:
The system dynamically adjusts audio output parameters such as volume, frequency, and timing based on real-time analysis of player responses and game state predictions. By changing these parameters adaptively rather than using fixed configurations, the system achieves high adaptability to different impairment levels while maintaining manageable device complexity through algorithmic control.
2Adaptability or versatility
If AI-based models are created for each impairment type, then adaptability improves, but computational resources increase
Solution Approach 1:
The system uses a universal reinforcement learning framework that can handle multiple impairment types and levels through a single adaptable model rather than requiring separate specialized models for each condition. This multi-functional approach allows the same computational infrastructure to serve diverse player needs, reducing overall computational resource requirements while maintaining personalization capabilities.
Solution Approach 2:
The system implements partial personalization by focusing computational resources on the most relevant audio output modifications for each player's specific needs rather than optimizing all possible game parameters. This selective approach provides sufficient personalization for effective gameplay while avoiding the excessive computational burden of complete customization across all game aspects.
3Ease of operation
If audio output is modified in real-time, then player engagement improves, but processing time increases
Solution Approach 1:
The system performs preliminary predictions of game states using audio information before actual game events occur. By anticipating upcoming game states and pre-calculating appropriate audio modifications, the system reduces real-time processing requirements while maintaining high player engagement through timely and relevant audio feedback adjustments.
Solution Approach 2:
The system implements continuous feedback loops where player responses to audio modifications are monitored and used to refine future adjustments. This feedback mechanism enables the system to learn optimal modification strategies over time, improving player engagement through increasingly accurate adaptations while reducing processing time as the model becomes more efficient at predicting effective modifications.
Data Source
AI summary
The present disclosure relates generally to a gaming system, device, and method supportive of a self-evolving, AI-based player and other behaviors. The gaming system, device, and method receive audio information associated with a current game state of a game during a gaming session; predict a game state based on the audio information; compare the predicted game state with the current game state to determine a set of differences between the predicted game state and the current game state; based on the determined set of differences, select, from a plurality of possible sets of modifications, a set of modifications to an audio output of a game in a gaming session; modify the audio output of the game in accordance with the selected set of modifications; and over successive time intervals, iteratively perform the predict, compare, select and modify operations to provide a game model substantially minimizing the determined set of differences.