Proficiency Agent Matching via Distributed Ledger Verification
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Solution Overview
Problem
Intelligent tutor systems face challenges in accurately quantifying user knowledge and proficiency assessment due to disparities in skills between systems and users, making it difficult to match users with systems of comparable skill levels.
Innovation Solution
The method involves initializing proficiency agents with scores, performing assessments between them, and using a rating system update function to match users with agents based on their proficiency scores, updating scores through encounter tensors and a competitive game algorithm, and storing metrics in a distributed ledger like blockchain for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different artificial intelligence agents are used to perform proficiency assessments, then assessment coverage and capability are improved, but it becomes difficult to quantify the difficulty of various proficiency assessments and compare scores across different agents
Solution Approach 1:
The patent transforms proficiency assessments into a standardized parameter space by converting scores from different agents into a common difficulty scale. This allows scores from multiple diverse agents to be compared and aggregated meaningfully, resolving the measurement precision issue while maintaining assessment versatility
Solution Approach 2:
The patent introduces an intermediary difficulty quantification layer that mediates between diverse proficiency agents and the final assessment results. This intermediary mechanism standardizes the output of different agents, enabling fair comparison and combination of scores across multiple agents without losing the benefits of diverse assessment capabilities
2Measurement precision
If proficiency assessments are performed between multiple agents to initialize scores, then score accuracy is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The patent performs preliminary proficiency assessments between agents during an initialization phase to establish accurate baseline scores before actual user assessments begin. This preliminary action ensures score accuracy is achieved upfront, reducing the need for continuous complex computations during operational use
Solution Approach 2:
The patent segments the assessment process into distinct phases: initialization phase where agents assess each other to establish baseline scores, and operational phase where agents assess users. This segmentation allows complex computations to be concentrated in the initialization phase, reducing ongoing system complexity while maintaining score accuracy
Data Source
AI summary
Techniques for assessing the proficiency of artificial intelligence agents and users in a given knowledge domain are described. A plurality of proficiency agents can be initialized with a plurality of proficiency scores, by performing a plurality of assessments between pairs of proficiency agents selected from the plurality of proficiency agents. A first client device associated with a first user is matched with a first proficiency agent of the plurality of proficiency agents, based on a first proficiency score associated with the first user and a second proficiency score of the plurality of proficiency scores corresponding to the first proficiency agent. Assessments results of an assessment performed between the first client device and the first proficiency agent are received, and a rating system update function is used to update the first proficiency score and the second proficiency score, based on the assessment results.