Cognitive Robot Knowledge Gap Assessment and Dynamic Learning
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Solution Overview
Problem
Cognitive robots often lack the necessary knowledge to perform specific activities, leading to incomplete or inaccurate results when attempting tasks, as they do not have the required learning or knowledge to execute them effectively.
Innovation Solution
A computer-implemented method that determines the required knowledge for a robot to perform an activity, compares it to the robot's current knowledge, and requests additional learning if needed, either retrieving it if available or requesting its creation if not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot attempts to perform activities without sufficient knowledge, then the robot can execute tasks, but the results are incomplete or inaccurate
Solution Approach 1:
The system performs preliminary knowledge assessment by comparing required knowledge against current robot knowledge before task execution. This preliminary action identifies knowledge gaps and triggers learning processes in advance, ensuring the robot has sufficient knowledge before attempting tasks, thereby improving accuracy without limiting versatility
2Adaptability or versatility
If the robot is equipped with extensive knowledge, then the robot can perform more activities, but the system complexity increases
Solution Approach 1:
The knowledge base is made dynamic through on-demand learning. Instead of statically pre-loading all possible knowledge, the system dynamically acquires knowledge only when needed for specific tasks. The learning manager continuously updates the robot's knowledge base by retrieving and integrating new information from external sources, allowing versatility to expand without permanently increasing system complexity
Solution Approach 2:
The knowledge management system is segmented into distinct functional modules: knowledge requirement identifier, knowledge comparator, learning determiner, and learning manager. This segmentation allows each component to handle specific aspects of knowledge management independently, reducing overall system complexity while enabling comprehensive knowledge handling capabilities
3Reliability
If the robot requests additional learning for every unknown task, then the robot improves accuracy, but the time required to execute tasks increases
Solution Approach 1:
The system applies partial learning actions by comparing required knowledge against current knowledge and requesting only the specific additional learning needed for the particular task at hand, rather than进行全面学习. This targeted approach minimizes time loss while ensuring sufficient accuracy for each specific task
Data Source
AI summary
A computer-implemented method includes establishing, by a computer device, an activity to be performed by a robot; determining, by the computer device, a required knowledge that is required for the robot to perform the activity; comparing, by the computer device, the required knowledge to a current knowledge of the robot to establish an additional learning that is needed for the robot to perform the activity; requesting, by the computer device, the additional learning; directing, by the computer device, retrieval of the additional learning to the robot if the additional learning is available for retrieval; and requesting, by the computer device, that the additional learning be created if the additional learning is not available for retrieval.