Expert System Optimization via Machine Learning Feedback
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Solution Overview
Problem
Traditional expert systems are limited in their ability to adapt and improve over time, relying on manual tuning and lacking explainability, which restricts their accuracy and effectiveness in providing performance feedback and interventions for employee performance optimization.
Innovation Solution
Integration of machine learning models that learn from user data to adjust and optimize expert systems by refining metrics, causes, and interventions, allowing for human-readable decision branches and continuous improvement while maintaining explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional expert systems are used with manual tuning, then human readability and understandability are maintained, but adaptability and accuracy over time deteriorate
Solution Approach 1:
The expert system automatically optimizes itself by using its own output data to train machine learning models that generate adjustments to its decision branches, eliminating the need for external manual tuning and enabling continuous self-improvement over time
Solution Approach 2:
The system uses feedback loops where the expert system's output is fed into machine learning models that analyze performance data and generate adjustments back to the expert system, creating a closed-loop optimization process that continuously improves accuracy
2Measurement precision
If traditional machine learning models are used, then accuracy and adaptability improve, but explainability and human readability deteriorate
Solution Approach 1:
Machine learning models serve as intermediaries that process complex data patterns and translate them into structured adjustments for the expert system's decision branches, maintaining explainability while leveraging ML's pattern recognition capabilities for improved accuracy
Solution Approach 2:
The system segments the optimization task into distinct components: the expert system maintains human-readable decision logic while machine learning models handle pattern recognition and adjustment generation, allowing each component to specialize without compromising overall explainability
3Measurement precision
If expert systems are optimized continuously with user data, then accuracy improves, but system complexity increases
Solution Approach 1:
The system dynamically adjusts its complexity by using machine learning models to optimize only specific aspects of the expert system (decision branch adjustments) rather than redesigning the entire system, allowing accuracy improvement with controlled complexity increase
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning optimization of expert systems. Techniques described herein include systems and methods to train and use machine learning networks to supplement expert systems. In some cases, expert systems can be configured to perform one or more operations, such as providing corrections for employee performance. Machine learning networks can obtain output from expert systems and learn corrections for the expert systems based on the provided output.