Automated Labeling Functions for Video Game Anomaly Detection
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Solution Overview
Problem
The creation of large hand-labeled training sets for machine learning models is time-consuming and expensive, and experts in application domains like video games lack the necessary knowledge to build these models, hindering the development of effective classification systems for behaviors or anomalies in real-time operations.
Innovation Solution
A computer-implemented method that allows users to transform domain knowledge into a machine learning model through a user interface, where labeling functions are converted into executable code and applied with an automated machine learning module to generate a robust model for real-time video game operations, enabling non-data scientist experts to create and deploy machine learning models for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-labeled training sets are created using expert labelers, then the quality and accuracy of labeled data improve, but the time consumption and cost increase significantly
Solution Approach 1:
The system enables domain experts to automatically generate labeled training data through automated labeling functions that they define themselves, eliminating the need for manual labeling by specialized annotators. The experts use their domain knowledge to create labeling rules that automatically process and label game data.
Solution Approach 2:
Automated labeling functions serve as intermediaries between domain experts and the machine learning model training process. These functions translate expert domain knowledge into automated labeling operations, bridging the gap between expert intuition and systematic data preparation.
2Measurement precision
If domain experts create machine learning models directly, then the domain knowledge accuracy improves, but the technical complexity and difficulty of model creation increase
Solution Approach 1:
The system introduces automated labeling functions and machine learning pipelines as intermediaries that handle the technical complexity of model creation. Domain experts interact with simplified interfaces that automatically manage the sophisticated processes of data preprocessing, feature engineering, and model training.
Solution Approach 2:
The system replaces the manual mechanical process of data preparation and model training with automated computational processes. The automated labeling functions and machine learning pipelines substitute for the complex manual operations that would otherwise be required to create and train models.
3Productivity
If automated labeling functions are used, then the productivity and speed of data preparation improve, but the initial setup complexity and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by automatically generating labeling functions and preparing training data before the actual model training process. This upfront automation of data preparation tasks enables faster model development cycles, as the computational infrastructure and labeling logic are established in advance.
Data Source
AI summary
A computer-implemented method is provided of allowing a user to automatically transform domain knowledge into a machine learning model to be used in real-time operation of video games. The method comprises providing a user interface which allows a user to define domain knowledge relating to a video game by specifying one or more labeling functions; transforming the labeling functions into executable code; labeling raw data relating to the video game using the executable code to obtain labeled data; and applying an automated machine learning module to the labeled data to obtain a machine learning model.