Symbolic Model Training via Active Learning
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Solution Overview
Problem
Traditional symbolic models for AI planning domains are typically manually written by software developers, limiting their effectiveness and efficiency, and existing methods for training these models struggle with processing vast amounts of image data autonomously.
Innovation Solution
A system that employs active machine learning to train symbolic models by characterizing planning domains as digital image sequences, using a combination of image recognition models and deep learning algorithms to generate and update training data, thereby enhancing model accuracy through semi-supervised classification techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If symbolic models are manually written by software developers, then the models can be created with domain expertise, but the process is time-consuming and limits effectiveness and efficiency
Solution Approach 1:
The patent replaces the manual mechanical process of writing symbolic models by software developers with an automated machine learning system. The system uses image recognition models and deep learning algorithms to automatically generate and train symbolic models from image data, eliminating the need for manual model creation while maintaining or improving accuracy through active learning and semi-supervised classification techniques.
2Reliability
If existing methods train symbolic models with vast amounts of image data, then comprehensive training is achieved, but the processing efficiency and autonomous capability are insufficient
Solution Approach 1:
The patent implements a self-service training system where the machine learning model autonomously processes image data without requiring manual intervention. The system automatically performs data preprocessing, feature extraction, model training, and evaluation, enabling comprehensive training on vast amounts of image data while significantly improving processing efficiency through automated workflows and efficient algorithms.
Solution Approach 2:
The patent applies preliminary action by pre-processing image data and preparing training datasets before the main training process. The system performs initial data cleaning, augmentation, and organization to optimize the training process, allowing the model to efficiently process comprehensive image data without manual intervention during the actual training phase.
3Measurement precision
If active machine learning is used to label and update training data, then model accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the trained model's performance is continuously evaluated and used to identify areas for improvement. The active learning system automatically selects uncertain or misclassified samples, retrieves relevant image data, and uses it to update and retrain the model, creating a closed-loop feedback system that improves accuracy while managing complexity through automated processes.
Data Source
AI summary
Techniques regarding generating and/or training one or more symbolic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train a symbolic model via active machine learning. The symbolic model can characterize a formal planning language for a planning domain as a plurality of digital image sequences.