View source for Nec corporation (20240102813). ROUTE DESIGN SYSTEM, COST FUNCTION LEARNING DEVICE, DESIGNED ROUTE OUTPUT DEVICE, METHOD, AND PROGRAM simplified abstract

=ROUTE DESIGN SYSTEM, COST FUNCTION LEARNING DEVICE, DESIGNED ROUTE OUTPUT DEVICE, METHOD, AND PROGRAM=


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Asako Fujii of Tokyo (JP)|Asako Fujii of Tokyo (JP)]][[Category:Asako Fujii of Tokyo (JP)]]

[[:Category:Riki Eto of Tokyo (JP)|Riki Eto of Tokyo (JP)]][[Category:Riki Eto of Tokyo (JP)]]

[[:Category:Yuki Chiba of Tokyo (JP)|Yuki Chiba of Tokyo (JP)]][[Category:Yuki Chiba of Tokyo (JP)]]

[[:Category:Norihito Oi of Tokyo (JP)|Norihito Oi of Tokyo (JP)]][[Category:Norihito Oi of Tokyo (JP)]]

==ROUTE DESIGN SYSTEM, COST FUNCTION LEARNING DEVICE, DESIGNED ROUTE OUTPUT DEVICE, METHOD, AND PROGRAM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240102813 titled 'ROUTE DESIGN SYSTEM, COST FUNCTION LEARNING DEVICE, DESIGNED ROUTE OUTPUT DEVICE, METHOD, AND PROGRAM

==Simplified Explanation==

The function input means accepts input of a cost function that calculates a cost incurred in at least one of a selection of a candidate relay point and a design of a route, the cost function being represented as a linear sum of terms weighted by degree of importance attached to each of the features that an expert is assumed to intend when selecting the candidate relay point and design of the route. The learning means learns the cost function by inverse reinforcement learning using training data that includes relay point information which is data that maps information indicating a relay point with surrounding information of the relay point and usage information of the relay point, and route result information which is result data of a route that pass through each relay point.

* The innovation involves accepting input of a cost function that calculates costs for selecting a candidate relay point and designing a route.
* The cost function is represented as a linear sum of terms weighted by the importance of each feature considered by an expert when making selections.
* The learning process involves inverse reinforcement learning using training data that includes relay point information and route result information.

== Potential Applications ==
This technology could be applied in:
* Transportation planning
* Logistics optimization
* Network routing algorithms

== Problems Solved ==
This technology helps in:
* Efficiently selecting relay points
* Optimizing route designs
* Improving decision-making processes in transportation and logistics

== Benefits ==
The benefits of this technology include:
* Cost savings in transportation operations
* Enhanced efficiency in route planning
* Improved resource allocation

== Potential Commercial Applications ==
Optimizing Transportation Costs and Routes: Leveraging Advanced Cost Function Analysis

== Possible Prior Art ==
One possible prior art could be the use of reinforcement learning in transportation planning and optimization.

== Unanswered Questions ==
=== How does this technology compare to traditional cost function analysis methods in transportation planning? ===
This article does not provide a direct comparison between this technology and traditional cost function analysis methods in transportation planning.

=== What are the specific features and factors considered in the cost function for selecting relay points and designing routes? ===
The article does not delve into the specific features and factors considered in the cost function for selecting relay points and designing routes.


==Original Abstract Submitted==

the function input means  accepts input of a cost function that calculates a cost incurred in at least one of a selection of a candidate relay point and a design of a route, the cost function being represented as a linear sum of terms weighted by degree of importance attached to each of the features that an expert is assumed to intend when selecting the candidate relay point and design of the route. the learning means  learns the cost function by inverse reinforcement learning using training data that includes relay point information which is data that maps information indicating a relay point with surrounding information of the relay point and usage information of the relay point, and route result information which is result data of a route that pass through each relay point.



[[Category:G01C21/34]]
[[Category:G06N20/00]]