Adaptive Video Game Difficulty via Machine Learning

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Solution Overview

Problem

Video games lack variability after initial completion, leading to a diminished player experience, as existing technologies do not effectively adjust the difficulty level of non-player opponents to enhance replayability.

Innovation Solution

Implementing machine learning, specifically deep learning artificial neural networks, to analyze player data and dynamically adjust the difficulty level of non-player video game opponents, providing tailored gameplay experiences and rewarding players based on their performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine learning is used to dynamically adjust difficulty levels, then player engagement and replayability are improved, but device complexity and computational requirements increase

Engineering Contradiction:

Improvedifficulty adjustment adaptabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces machine learning models as intermediary components between player actions and game responses. These models process player behavior data and generate difficulty adjustments, acting as a mediator that translates raw player interactions into adaptive gameplay parameters without requiring complex hard-coded rule systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically changes game parameters such as enemy health, attack strength, and spawn rates based on machine learning predictions of player skill levels. This allows the game to adapt difficulty by modifying numerical parameters rather than restructuring entire game systems, reducing the complexity burden.

Inventive Principle:

Principle #35Parameter changes

2Ease of operation

If player data is collected and analyzed to personalize gameplay, then player experience is improved, but data processing requirements and computational resources increase

Engineering Contradiction:

Improvepersonalized gameplay experienceVSAvoidcomputational energy consumption

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary data processing during gameplay by continuously collecting and analyzing player actions in real-time. Machine learning models are trained incrementally on accumulated data rather than requiring batch processing of entire datasets, reducing peak computational energy demands while maintaining personalization quality.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If difficulty levels are adjusted based on successful methods identified by machine learning, then gameplay balance is improved, but measurement and analysis complexity increase

Engineering Contradiction:

Improvegameplay balance reliabilityVSAvoidplayer strategy analysis difficulty

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system implements continuous feedback loops where player performance data is collected, analyzed by machine learning models to identify successful strategies, and then used to adjust difficulty parameters. This closed-loop feedback mechanism automatically detects and responds to player strategies without requiring manual analysis, reducing measurement complexity while improving gameplay balance reliability.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11065545B2Use of machine learning to increase or decrease level of difficulty in beating video game opponent

Publication Date: 2021.07.20 SONY INTERACTIVE ENTERTAINMENT LLC

AI summary

Machine learning is used to determine one or more video game players' ability to defeat a non-player video game opponent, such as a boss. The level of difficulty in beating the video game opponent may then be increased or decreased based on the players' ability to beat the video game opponent. In some examples, another player may then opt in to the increased or decreased level of difficulty, or may choose to play the video game without the increased or decreased level of difficulty.