AI Emulating Human Playstyles via Gameplay Logs
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Solution Overview
Problem
Current AI systems in video games rely on hardcoded algorithms, which are time-consuming to develop, often behave unnaturally, and fail to emulate human creativity and instinct, limiting their ability to make complex decisions and adapt to various gameplay scenarios.
Innovation Solution
A system that generates AI models using machine learning techniques, processing positional and non-positional data from gameplay logs to control NPCs, allowing for the emulation of human playstyles and behaviors, enabling more natural and adaptive AI decision-making without extensive custom coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardcoded algorithms are used to control AI actors, then the AI behavior can be precisely controlled through mathematical and logical comparisons, but the development time is excessive and the AI behaves unnaturally
Solution Approach 1:
The patent copies human playstyle patterns from gameplay logs into AI models. Instead of programming AI behavior through hardcoded rules, the system records and replicates actual human player decisions and actions, allowing the AI to emulate natural human behavior patterns while reducing development time.
Solution Approach 2:
The patent replaces the mechanical system of hardcoded mathematical and logical comparisons with a machine learning-based AI model. The AI model processes gameplay data and generates control decisions through learned patterns rather than predetermined rules, achieving more natural behavior with less manual programming.
2Ease of operation
If hardcoded rules are used to define AI tasks and decisions, then the AI can perform specific mathematical or logical comparisons, but the AI fails to emulate human creativity and instinct
Solution Approach 1:
The AI model serves itself by learning from gameplay logs and automatically generating control decisions based on patterns it discovers in the data. Rather than requiring programmers to encode every possible scenario, the AI autonomously adapts its behavior by processing and learning from actual human gameplay, emulating creativity and instinct naturally.
Solution Approach 2:
The patent changes the fundamental parameter of AI decision-making from fixed mathematical thresholds to dynamic learned patterns. The AI model adjusts its decision parameters based on the gameplay data it processes, allowing it to emulate the variability and adaptability of human creativity and instinct rather than following rigid predefined rules.
3Adaptability or versatility
If AI models are trained using machine learning techniques, then the AI can adapt to various gameplay scenarios and emulate human playstyles, but the system complexity increases
Solution Approach 1:
The patent creates a universal AI training system that can handle multiple game types and scenarios through a single machine learning framework. The system processes gameplay logs from various sources and adapts to different game contexts, reducing the need for separate AI implementations for each scenario while managing complexity through standardized data processing pipelines.
Data Source
AI summary
An artificially intelligent entity can emulate human behavior in video games. An AI model can be made by receiving gameplay logs of a video gameplay session, generating, based on the gameplay data, first situational data indicating first states of the video game, generating first control inputs provided by a human, the first control inputs corresponding to the first states of the video game, training a first machine learning system using the first situational data and corresponding first control inputs, and generating, using the first machine learning system, a first artificial intelligence model. The machine learning system can include a convolutional neural network. Inputs to the machine learning system can include a retina image and/or a matrix image.