Automated Game Coaching via Performance Models
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Solution Overview
Problem
Online gaming players often struggle to improve their skills, leading to diminished enjoyment and potential disengagement due to the complexity of modern games and lack of effective methods for enhancing performance.
Innovation Solution
A game coaching system that generates performance models using player data to provide personalized recommendations for improving gameplay, utilizing machine learning algorithms to analyze gameplay metrics and suggest actionable changes that can enhance player performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If players attempt to improve their skills through self-study and practice, then skill improvement may occur, but the complexity of modern games and lack of structured guidance makes it difficult to become more skilled over time
Solution Approach 1:
The system implements automated feedback by analyzing player gameplay data and providing personalized coaching recommendations. The coaching system processes player actions, performance metrics, and game states to generate actionable feedback that helps players improve specific skills. This structured feedback loop addresses the lack of guidance by automatically evaluating player performance and suggesting improvements based on data-driven insights.
Solution Approach 2:
The system enables self-service coaching by allowing players to receive personalized guidance without human intervention. The automated coaching system independently analyzes player data, generates recommendations, and delivers feedback directly to players. This self-service approach makes skill improvement accessible to all players regardless of access to human coaches, while the system handles the complexity of analysis and recommendation generation autonomously.
2Productivity
If players continue to play without sufficient skill improvement, then they maintain current engagement levels, but they experience diminished enjoyment and become more prone to disengage from the online game
Solution Approach 1:
The system applies preliminary action by proactively identifying players who need skill improvement before they disengage. The coaching system continuously monitors player performance and detects when players are struggling, then preemptively provides targeted coaching recommendations. This early intervention prevents the decline in enjoyment and disengagement that would otherwise occur, maintaining player retention by addressing skill gaps before they become critical.
Solution Approach 2:
The system implements continuous feedback loops that monitor player engagement and performance metrics. By analyzing gameplay data in real-time, the system identifies players whose enjoyment may be declining due to lack of skill improvement and provides timely coaching interventions. This feedback mechanism ensures players receive guidance when it is most needed, thereby maintaining both engagement levels and long-term retention.
3Ease of operation
If a comprehensive coaching system is implemented to provide personalized recommendations, then player skill improvement and engagement increase, but the system complexity and data processing requirements increase
Solution Approach 1:
The system introduces an intermediary layer between raw gameplay data and player recommendations. The coaching system acts as a mediator that automatically processes complex game data, identifies skill gaps, and translates them into actionable coaching advice. This intermediary processing layer handles the complexity of data analysis and recommendation generation, while players receive simplified, easy-to-follow guidance without needing to understand the underlying complexity.
Solution Approach 2:
The system replaces manual coaching mechanics with automated computational processes. Instead of requiring human coaches to analyze gameplay and provide recommendations, the system uses machine learning algorithms and data processing to automatically generate personalized coaching advice. This substitution reduces the operational complexity for players while the system's computational infrastructure handles the complex analysis tasks.
Data Source
AI summary
A game coaching system identifies gameplay data associated with online game players of an online game and determines, based at least in part on the gameplay data, a performance model to predict performance metric(s) of individual players. The performance model may be used to conduct a sensitivity analysis to determine which perturbations to individual parameters of gameplay data result in improvements to the performance metric(s) of a player. This sensitivity analysis may be used to identify action(s) to recommend to the player to improve his or her gameplay performance. The action(s) that are recommended to the player may be decided based at least in part on the predicted impact of those action(s) on player performance and/or the ease of implementing the action(s). The game coaching system may provide updated action recommendations to players to allow the player to improve his or her gameplay performance over time.