Expert System Anomaly Prediction Feedback Customization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current expert systems for anomaly prediction lack user feedback and customization capabilities, limiting their ability to adapt to different environments and provide tailored predictions that are accurate and meaningful for specific users.
Innovation Solution
The system continuously accepts user feedback and allows self-customization, dynamically adjusting to each user's requirements by filtering predictions based on user preferences and historical feedback, allowing users to modify the type and content of information fed into the expert system and the predictions presented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If expert systems use fixed rules for anomaly prediction, then the system structure remains simple, but the system cannot adapt to different user environments and requirements
Solution Approach 1:
The expert system transitions from static fixed rules to dynamic adaptive rules that automatically adjust based on user feedback and environmental conditions. The system continuously learns and modifies its prediction models to adapt to different user requirements while maintaining a manageable complexity through automated learning processes.
Solution Approach 2:
The system implements feedback mechanisms where user interactions and prediction outcomes are continuously collected and used to refine the prediction models. This feedback loop enables the system to adapt to user requirements over time without requiring complex manual reconfiguration, resolving the contradiction between adaptability and complexity.
2Ease of operation
If the system provides all predicted anomalies to users, then completeness of information is achieved, but users are overwhelmed with irrelevant or low-value predictions
Solution Approach 1:
The system applies different filtering and prioritization strategies to different users based on their specific roles, historical behavior, and preferences. Rather than applying a uniform filtering approach, each user receives customized information presentation that highlights relevant anomalies while maintaining the underlying completeness of the prediction system.
Solution Approach 2:
The system initially provides comprehensive predictions but progressively filters and prioritizes them based on user feedback and interaction patterns. This partial action approach allows the system to maintain information completeness in the background while presenting only the most relevant predictions to users, improving ease of operation without permanent loss of information.
3Adaptability or versatility
If the expert system is trained only with labeled training data, then the training process is simple and controlled, but the system cannot continuously learn and adapt in production environments
Solution Approach 1:
The system extends the learning process from discrete training phases to continuous operation in production environments. By implementing ongoing learning mechanisms that process real-world data and user feedback, the system maintains and improves its predictive capabilities continuously without requiring complex retraining deployments, balancing adaptability with deployment simplicity.
Solution Approach 2:
The expert system performs self-learning and self-improvement in production environments by automatically processing new data and adjusting its models without requiring external retraining interventions. This self-service capability enables continuous adaptation while simplifying the deployment process, as the system evolves autonomously after initial deployment.
Data Source
AI summary
Systems and methods may include receiving, by an expert system, performance data for a monitored system. The systems and methods may include generating a prediction for the monitored system in response to determining that the performance data satisfies a condition. The prediction may identify an anomaly that is predicted to occur. The systems and methods may include receiving, by a filter system, the prediction, information identifying the condition, and user information. The user information may include user preference information and user feedback information. The systems and methods may include determining a filter criteria based on the user information. The filter criteria may be based on the preferences for predictions to be provided to the user and on the historical user feedback regarding the historical predictions. The systems and methods may include providing the prediction to the user in response to determining that the particular prediction satisfies the filter criteria.