CogBot Learning Curve Analysis via Comparative Feedback
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Solution Overview
Problem
Existing cognitive software robots (CogBots) lack a method to effectively evaluate and predict their learning maturity, making it difficult to determine if their learning paths are positive and aligned with other CogBots, which is crucial for real-world performance improvement.
Innovation Solution
A computer-implemented method generates a graph of historic learning curves for a primary CogBot and secondary CogBots, creating a best probable learning curve predictive of future learning, allowing for the assessment of the primary CogBot's current learning status and maturity level by analyzing distances between reference learning curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CogBots learn autonomously over time to improve functionality, then their knowledge and capability are enhanced, but there is no method to evaluate or predict their learning maturity, making it difficult to determine if their learning paths are positive
Solution Approach 1:
The system implements feedback by comparing the primary CogBot's learning curve against historical learning curves from multiple secondary CogBots. This comparison provides evaluative feedback on whether the primary CogBot's learning path is positive or negative, enabling assessment of learning maturity without interfering with autonomous learning capability.
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between the autonomous learning process and the need for assessment. This intermediary system generates and compares learning curves, using distance metrics to evaluate learning maturity while allowing the CogBots to continue learning independently.
2Measurement precision
If the system tracks and analyzes learning curves over time to evaluate learning maturity, then learning status can be assessed, but this requires collecting and processing historic learning data from multiple CogBots
Solution Approach 1:
The learning curve generation and comparison system serves multiple functions: it tracks individual CogBot learning progress, enables maturity assessment through historical comparison, and provides evaluative feedback on learning paths. This multi-functional approach reduces the need for separate specialized systems for each function.
3Adaptability or versatility
If the primary CogBot follows a unique learning path, then it can develop specialized knowledge, but it becomes difficult to determine if the learning path is aligned and progressing positively without reference to other CogBots
Solution Approach 1:
The system provides feedback on learning alignment by calculating the distance between the primary CogBot's learning curve and historical learning curves from secondary CogBots. This feedback mechanism preserves learning path flexibility while providing information about alignment and positive progression without constraining the unique learning path.
Data Source
AI summary
Systems and methods for analyzing machine learning of cognitive software robots (CogBots) over time are provided. In implementations, a method includes generating, by a computing device, a graph of historic learning curves based on historic learning data over time for a subject obtained from a primary cognitive software robot (CogBot) and at least one secondary CogBot; generating, by the computing device, a best probable learning curve based on the historic learning curves of the graph, wherein the best probable learning curve is predictive of future learning by the primary CogBot for the subject; and generating, by the computing device, information regarding a current status of the learning of the primary CogBot based on the best probable learning curve.