Reflective Learning System for Cognitive Decision-Making
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Solution Overview
Problem
Conventional machine learning systems are inadequate for reflective learning in cognitive decision-making processes due to their inability to account for continuous man-machine interactions and dynamic changes in the decision-making environment, leading to unpredictable performance degradation.
Innovation Solution
A system and method for reflective learning that receives input data, computes deviations in user behavior, classifies business opportunities and strategies, identifies inaccurate algorithms and metadata, and executes retraining or modification to enable continuous learning in cognitive decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning systems are used for cognitive decision-making, then the system can process historical data and learn models, but the system cannot reflect upon contextual decision-making processes and cannot adapt to continuous man-machine interactions and dynamic changes
Solution Approach 1:
The patent implements a reflective learning mechanism that continuously monitors user interactions and decision outcomes, feeding this information back into the system to adjust and refine decision-making models. This feedback loop enables the system to adapt to dynamic changes while maintaining reliable performance through continuous self-improvement based on actual usage patterns.
Solution Approach 2:
The system transitions from static historical data processing to dynamic contextual analysis by incorporating real-time user behavior patterns, interaction contexts, and evolving decision environments. This dynamic approach allows the machine learning models to continuously adapt their parameters and structures based on current operational conditions rather than relying solely on historical patterns.
2Measurement precision
If machine learning models are continuously applied without reflection, then processing speed is maintained, but accuracy degrades due to inability to account for contextual changes and model interdependencies
Solution Approach 1:
Instead of complete periodic retraining of all models, the system applies partial updates only to specific models or components that show degradation or contextual relevance changes. This selective retraining approach maintains high decision accuracy by focusing computational resources on areas that need improvement while minimizing time loss through targeted rather than comprehensive model updates.
3Measurement precision
If the system monitors and reflects on all user interactions, then learning accuracy improves, but system complexity and computational load increase
Solution Approach 1:
The reflective learning system is segmented into distinct functional modules: interaction monitoring components, contextual analysis modules, model evaluation units, and selective retraining mechanisms. This segmentation allows the system to achieve high learning accuracy through comprehensive monitoring while managing complexity by organizing functions into independent, manageable modules that can operate and be maintained separately.
Data Source
AI summary
The present disclosure relates to system(s) and method(s) for reflective learning in a cognitive decision-making process. In one embodiment, the method comprises receiving input data and computing a first deviation in user behavior based on input data. The method further comprises classifying the business opportunities and the strategies as one of an accurate business opportunities or an inaccurate business opportunities and an accurate strategies or an inaccurate strategies based on comparison of the first deviation with a predefined threshold and identifying one or more algorithm and metadata associated with the inaccurate business opportunities and the inaccurate strategies. The method furthermore comprises executing one of a retraining of the one or more algorithm, a modifying the metadata associated with the inaccurate business opportunities and the inaccurate strategies or generating a new algorithm thereby enabling reflective learning in a cognitive decision-making process.