Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Natural language-based robot deep interacting and reasoning method and device

A technology of natural language and reasoning methods, applied in the field of deep interaction and reasoning of robots, can solve problems such as the inability to analyze users, lack of autonomy, and reasoning mechanisms without analytical capabilities, so as to improve the recognition rate, reduce the search range, and improve the market The effect of application prospect and development potential

Active Publication Date: 2016-10-26
WUHAN UNIV OF SCI & TECH
View PDF3 Cites 49 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the reasoning mechanism based on case-based reasoning does not have the ability to analyze, cannot analyze the user's unclear use and give feedback and guidance, and is not autonomous.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Natural language-based robot deep interacting and reasoning method and device
  • Natural language-based robot deep interacting and reasoning method and device
  • Natural language-based robot deep interacting and reasoning method and device

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0060] S31: Segment the text obtained in S1 using a tokenizer. Example 1: The text converted by the user input voice is: "grab an apple", after word segmentation by the tokenizer, the result is: "grab / one / apple / ".

example 2

[0061] S32: Match each word after word segmentation with the case base, if no similar case is retrieved, create a new case; if similar case is retrieved, return similar case:, and calculate the number of initial case attributes. Example 2: Case: "grab an apple", the initial attributes of the case are: object quantity and object name, then the initial case attribute quantity value is 2. When the number of initial case attributes is greater than 0, map matching is performed; when the number of initial case attributes is equal to 0, the input is invalid and the robot actively guides.

[0062] S4: In-depth dialogue and 3D scene interaction, the specific process is as follows Figure 4 Shown:

[0063] S41: map matching;

[0064] S411: The system needs to obtain high-quality semantic map information of the working environment through 3D visual environment perception. This design scheme uses Kinect to extract 3D point cloud images and establishes a CSHOT object model for feature m...

example 3

[0070] Example 3: There is a big red apple in the map file. The user says to the robot: "grab an apple". The corresponding situation for map matching is: the number of objects in the scene is equal to the number required by the user, but when performing the expected analysis, the objects If the attribute value of the placed destination name is empty, the expectation is incomplete, and the method in S43 needs to be used for user guidance.

[0071] S43: User guidance

[0072] When the expectations obtained by user expectations analysis are incomplete, user guidance should be carried out. Each attribute node in the case storage has a corresponding dialogue generation function, and the collection of these dialogue generation functions constitutes the bootstrap library. In example 3, the expectation is incomplete. At this time, when searching the guide library, the robot will ask the user: "Which basket do you want to put in" according to the default attributes, and then complete ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention discloses a natural language-based robot deep interacting and reasoning method and device. The method comprises 1) a speech identification step of receiving the user speech input, and processing an input signal to obtain the text information; 2) a case attribute obtaining step of carrying out the participle processing on the text obtained in the step 1), and then carrying out the similarity matching on the text after participle and the cases in a case base to extract the attributes of the cases; 3) a deep dialogue and three dimensional scene interaction step of if the intention of the user obtained according to the case attributes extracted in the step 2) is not complete, repeatedly guiding a user by combining a real-time map file obtained by a Kinect sensor until the complete intention is obtained, and then generating a solution scheme aiming at a working task of the user complete intention; a speech synthesis step of displaying the obtained solution scheme in a text format, synthesizing the speech, and feeding back to the user via a sound device. During an interaction process of the present invention, a robot and the user both use a natural language.

Description

technical field [0001] The present invention relates to artificial intelligence technology, in particular to a method and device for in-depth interaction and reasoning of robots based on natural language. Background technique [0002] In recent years, with the rapid development of intelligent robots, people expect robots to complete various tasks in complex environments through dialogue. Using natural language to communicate with machines has been what people have been pursuing for a long time: people can use their most accustomed language to operate robots without spending a lot of time and energy learning various complex computer languages. [0003] In this process, the intelligent robot system needs to understand natural language, understand user expectations, and have a reasoning mechanism to reason, solve and learn real-time problems. Among the current research results, the representative reasoning mechanisms include Rule-Based Reasoning (RBR), Procedural Reasoning Sys...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06N3/00G06N5/04G06F17/27G06K9/00G10L15/18G10L15/02G10L15/14G10L15/26G10L21/0208G10L25/54G10L13/08
CPCG06N3/008G06N5/04G10L13/08G10L15/02G10L15/144G10L15/148G10L15/1822G10L15/26G10L21/0208G10L25/54G06F40/289G06F40/30G06V20/10
Inventor 闵华松李潇齐诗萌林云汉周昊天
Owner WUHAN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com