AI Agent Team Selection via Cognitive Skill Mapping
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Solution Overview
Problem
Existing systems fail to effectively complement the cognitive abilities of workers to accomplish tasks efficiently, as they do not assess the cognitive skills of each worker in a group and recognize the need for additional cognitive abilities, leading to suboptimal task completion.
Innovation Solution
A method and system that utilize machine learning to identify and extract cognitive skills from task descriptions, generate a group cognitive map, and select a team of workers including AI agents to accomplish tasks, ensuring the necessary skills are aligned with the task requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to match cognitive skills to tasks through individual training, then individual cognitive abilities can be improved, but the system cannot assess the cognitive ability of each worker in a group and recognize the need for additional cognitive abilities, leading to suboptimal task completion
Solution Approach 1:
The patent introduces AI agents as intermediaries between human workers and task requirements. These AI agents possess complementary cognitive abilities that bridge gaps in human team capabilities, enabling the system to both assess individual cognitive skills and provide additional capabilities through the AI intermediaries.
Solution Approach 2:
The system creates a multi-functional team composition that includes both human workers and AI agents. This universal team structure can adapt to various task requirements by combining human cognitive strengths with AI computational strengths, allowing the same system to handle diverse task types effectively.
2Productivity
If more workers are added to accomplish a task, then task completion capacity increases, but the complexity of assessing and matching cognitive skills of each worker increases
Solution Approach 1:
The patent replaces manual cognitive skill assessment and matching processes with automated machine learning systems. The ML-based cognitive skill extractor automatically analyzes worker profiles and task requirements, eliminating the need for complex manual evaluation processes while scaling to large teams.
Solution Approach 2:
The system transforms the complexity problem by changing the assessment parameters from detailed individual cognitive profiles to extracted cognitive skill tags. This parameter transformation simplifies the matching process while maintaining accuracy, allowing the system to handle large numbers of workers efficiently.
3Productivity
If AI agents are used to complement human cognitive abilities, then task efficiency and productivity improve, but the system complexity increases
Solution Approach 1:
The patent segments the cognitive capability system into distinct modules: human worker cognitive skills, AI agent cognitive abilities, and a matching mechanism. This segmentation allows each component to be independently developed and optimized, reducing overall system complexity while maintaining high productivity.
Data Source
AI summary
The present invention provides a method, system, and computer program for selecting a plurality of workers to accomplish a task. The method includes: identifying a task from a description; extracting at least one cognitive skill from the description of the task using machine learning methods; generating a group cognitive map which includes the at least one cognitive skill; and selecting a plurality of workers to accomplish the task based on at least the group cognitive map, wherein the plurality of workers comprises at least on artificial intelligent (AI) agent.