Method and apparatus for generating task plan based on neural network

Patent metadata

Field	Value
Publication number	US-2022261644-A1
Application number	US-202217671134-A
Country	US
Kind code	A1
Filing date	Feb 14, 2022
Priority date	Feb 15, 2021
Publication date	Aug 18, 2022
Grant date	—

Abstract

Provided are a method and an apparatus for generating a task plan. A method of generating a task plan for performing an arbitrary task includes generating a search tree based on a plurality of task states of the task and a plurality of task actions for performing the task, estimating a recommended path for an internal connection of the search tree by inputting the plurality of task states and the plurality of task actions to a neural network based on the search tree, and generating the task plan by determining a target path that reaches from an initial state of the task to a target state of the task based on the recommended path.

First claim

What is claimed is:

1 . A method of generating a task plan for performing an arbitrary task, the method comprising:
generating a search tree based on a plurality of task states of the task and a plurality of task actions for performing the task; estimating a recommended path for an internal connection of the search tree by inputting the plurality of task states and the plurality of task actions to a neural network based on the search tree; and generating the task plan by determining a target path that reaches from an initial state of the task to a target state of the task based on the recommended path.

2 . The method of claim 1 , wherein the generating of the search tree comprises:
generating nodes corresponding to the plurality of task states; and generating the search tree by connecting the nodes via edges corresponding to the plurality of task actions.

3 . The method of claim 1 , wherein the estimating of the recommended path comprises:
generating a trained neural network by training the neural network based on the plurality of task states and the plurality of task actions; and estimating the recommended path based on the trained neural network.

4 . The method of claim 3 , wherein the generating of the trained neural network comprises:
generating a temporary task plan based on a heuristics; and generating the trained neural network by training the neural network based on the temporary task plan, the plurality of task states, and the plurality of task actions.

5 . The method of claim 3 , wherein the estimating of the recommended path comprises:
generating sequence data based on the plurality of task states, the plurality of task actions, and the task; and generating training data of the neural network by converting the sequence data.

6 . The method of claim 5 , wherein the generating of the training data comprises:
acquiring a hash code by performing a hash operation on a task state of the sequence data; generating an information vector by encoding a task action and a task of the sequence data; and generating the training data based on the hash code and the information vector.

7 . The method of claim 6 , wherein the generating of the information vector comprises acquiring a one-hot vector as the information vector by performing one-hot encoding on the task action and the task.

8 . The method of claim 1 , wherein the generating of the task plan comprises:
determining an edge connected to a front node in the search tree based on the recommended path; and determining a child node connected to the edge based on the edge.

9 . The method of claim 8 , wherein the determining of the edge comprises:
determining a recommended action type among the plurality of task actions based on the recommended path; and determining the edge based on the recommended action type.

10 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 .

11 . An apparatus for generating a task plan for performing an arbitrary task, the apparatus comprising:
a processor configured to generate a search tree based on a plurality of task states of the task and a plurality of task actions for performing the task, estimate a recommended path for an internal connection of the search tree by inputting the plurality of task states and the plurality of task actions to a neural network based on the search tree, and generate the task plan by determining a target path that reaches from an initial state of the task to a target state of the task based on the recommended path; and a memory configured to store an instruction that is executable by the processor.

12 . The apparatus of claim 11 , wherein the processor is configured to generate nodes corresponding to the plurality of task states, and generate the search tree by connecting the nodes via edges corresponding to the plurality of task actions.

13 . The apparatus of claim 11 , wherein the processor is configured to generate a trained neural network by training the neural network based on the plurality of task states and the plurality of task actions, and estimate the recommended path based on the trained neural network.

14 . The apparatus of claim 13 , wherein the process is configured to generate a temporary task plan based on a heuristics, and generate the trained neural network by training the neural network based on the temporary task plan, the plurality of task states and the plurality of task actions.

15 . The apparatus of claim 13 , wherein the processor is configured to generate sequence data based on the plurality of task states, the plurality of task actions, and the task, and generate training data of the neural network by converting the sequence data.

16 . The apparatus of claim 15 , wherein the processor is configured to acquire a hash code by performing a hash operation on a task state of the sequence data, generate an information vector by encoding a task action and a task of the sequence data, and generate the training data based on the hash code and the information vector.

17 . The apparatus of claim 16 , wherein the process is configured to acquire a one-hot vector as the information vector by performing one-hot encoding on the task action and the task.

18 . The apparatus of claim 11 , wherein the processor is configured to determine an edge connected to a front node in the search tree based on the recommended path, and determine a child node connected to the edge based on the edge.

19 . The apparatus of claim 18 , wherein the processor is configured to determine a recommended action type among the plurality of task actions based on the recommended path, and determine the edge based on the recommended action type.

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Cho Joonmyun

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0475

  Generative networks · CPC title