18086886. AUTOMATIC METAMODEL GENERATION FOR ARTIFICIAL INTELLIGENCE REASONING simplified abstract (Cisco Technology, Inc.)
Contents
AUTOMATIC METAMODEL GENERATION FOR ARTIFICIAL INTELLIGENCE REASONING
Organization Name
Inventor(s)
Hugo Latapie of Long Beach CA (US)
Ozkan Kilic of Long Beach CA (US)
Adam James Lawrence of Pasadfena CA (US)
Ramana Rao V. R. Kompella of Cupertino CA (US)
AUTOMATIC METAMODEL GENERATION FOR ARTIFICIAL INTELLIGENCE REASONING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18086886 titled 'AUTOMATIC METAMODEL GENERATION FOR ARTIFICIAL INTELLIGENCE REASONING
The abstract describes a patent application where a student agent identifies a topic of interest, asks questions to a teacher agent, receives answers, and uses the data to create a neuro-symbolic metamodel.
- Student agent identifies a topic of interest
- Student agent asks questions to teacher agent
- Teacher agent provides answer data
- Student agent uses answer data to create a neuro-symbolic metamodel
Potential Applications: - Educational technology - Artificial intelligence - Knowledge acquisition systems
Problems Solved: - Facilitates knowledge acquisition - Enhances learning process - Improves information retrieval
Benefits: - Efficient information gathering - Enhanced understanding of complex topics - Personalized learning experience
Commercial Applications: Title: "Enhanced Knowledge Acquisition System" This technology can be used in educational institutions, online learning platforms, and knowledge management systems to improve information retrieval and enhance the learning process.
Questions about the technology: 1. How does the student agent identify a topic of interest? 2. What is the role of the semantic reasoner in the neuro-symbolic metamodel?
Frequently Updated Research: Stay updated on advancements in artificial intelligence, knowledge acquisition systems, and educational technology to understand the latest trends and developments in the field.
Original Abstract Submitted
In one embodiment, a student agent identifies a topic of interest. The student agent issues a set of one or more questions to a teacher agent regarding the topic of interest. The student agent receives, from the teacher agent, answer data in response to the set of one or more questions. The student agent uses the answer data to generate a neuro-symbolic metamodel that comprises a semantic reasoner and a sub-symbolic layer.
