View source for 17922741. SERVER AND AGENT FOR REPORTING OF COMPUTATIONAL RESULTS DURING AN ITERATIVE LEARNING PROCESS simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

=SERVER AND AGENT FOR REPORTING OF COMPUTATIONAL RESULTS DURING AN ITERATIVE LEARNING PROCESS=


==Organization Name==

[[:Category:Telefonaktiebolaget LM Ericsson (publ)|Telefonaktiebolaget LM Ericsson (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson (publ)]]

==Inventor(s)==

[[:Category:Erik G. Larsson of Linköping (SE)|Erik G. Larsson of Linköping (SE)]][[Category:Erik G. Larsson of Linköping (SE)]]

[[:Category:Ema Becirovic of Linköping (SE)|Ema Becirovic of Linköping (SE)]][[Category:Ema Becirovic of Linköping (SE)]]

[[:Category:Zheng Chen of Norrköping (SE)|Zheng Chen of Norrköping (SE)]][[Category:Zheng Chen of Norrköping (SE)]]

[[:Category:Reza Moosavi of Linköping (SE)|Reza Moosavi of Linköping (SE)]][[Category:Reza Moosavi of Linköping (SE)]]

[[:Category:Erik Eriksson of Linköping (SE)|Erik Eriksson of Linköping (SE)]][[Category:Erik Eriksson of Linköping (SE)]]

[[:Category:Nicklas Johansson of Linköping (SE)|Nicklas Johansson of Linköping (SE)]][[Category:Nicklas Johansson of Linköping (SE)]]

==SERVER AND AGENT FOR REPORTING OF COMPUTATIONAL RESULTS DURING AN ITERATIVE LEARNING PROCESS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17922741 titled 'SERVER AND AGENT FOR REPORTING OF COMPUTATIONAL RESULTS DURING AN ITERATIVE LEARNING PROCESS

==Simplified Explanation==

The patent application describes a method for configuring agent entities to report computational results during an iterative learning process. The method involves a server entity configuring the agent entities with a computational task and a reporting condition. The agent entities compete for channel access to report computational results to the server entity only when an importance metric satisfies the reporting condition. The iterative learning process continues until a termination criterion is met.

* Agent entities are configured with a computational task and a reporting condition.
* Agent entities compete for channel access to report computational results.
* Reporting is only allowed when an importance metric satisfies the reporting condition.
* The iterative learning process continues until a termination criterion is met.

== Potential Applications ==
This technology can be applied in various fields where iterative learning processes are used, such as:

* Machine learning and artificial intelligence.
* Data analysis and pattern recognition.
* Optimization and decision-making algorithms.
* Robotics and autonomous systems.

== Problems Solved ==
The technology addresses the following problems:

* Efficient reporting of computational results during iterative learning processes.
* Prioritizing important computational results for reporting.
* Reducing unnecessary reporting and communication overhead.
* Streamlining the iterative learning process for faster convergence.

== Benefits ==
The technology offers several benefits:

* Improved efficiency and accuracy in reporting computational results.
* Optimal utilization of communication channels for reporting.
* Reduction in unnecessary reporting, leading to faster learning.
* Streamlined iterative learning process for better convergence.


==Original Abstract Submitted==

There is provided mechanisms for configuring agent entities with a reporting condition for reporting computational results during an iterative learning process. A method is performed by a server entity. The method comprises configuring the agent entities with a computational task and a reporting condition. The agent entities are to contend for channel access to report computational results of the computational task to the server entity only when an importance metric satisfies the reporting condition. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met.



[[Category:G06N3/0455]]
[[Category:G06N3/084]]