Natural-Language-Instructed Industrial Task Execution

2016 ◽  
Author(s):  
Rui Liu ◽  
Jeremy Webb ◽  
Xiaoli Zhang
Keyword(s):  
Natural Language ◽  
Humanoid Robot ◽  
Semantic Analysis ◽  
Advanced Manufacturing ◽  
Task Execution ◽  
Logic Modeling ◽  
Knowledge Based ◽  
Industrial Task ◽  
Manufacturing Machine

To effectively cooperate with a human, advanced manufacturing machines are expected to execute the industrial tasks according to human natural language (NL) instructions. However, NL instructions are not explicit enough to be understood and are not complete enough to be executed, leading to incorrected executions or even execution failure. To address these problems for better execution performance, we developed a Natural-Language-Instructed Task Execution (NL-Exe) method. In NL-Exe, semantic analysis is adopted to extract task-related knowledge, based on what human NL instructions are accurately understood. In addition, logic modeling is conducted to search the missing execution-related specifications, with which incomplete human instructions are repaired. By orally instructing a humanoid robot Baxter to perform industrial tasks “drill a hole” and “clean a spot”, we proved that NL-Exe could enable an advanced manufacturing machine to accurately understand human instructions, improving machine’s performance in industrial task execution.

scholarly journals Evolution of Semantic Similarity—A Survey

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Hybrid Methods ◽  
Research Work ◽  
Similarity Measures ◽  
Text Data ◽  
Knowledge Based ◽  
Open Research ◽  
Research Problems

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

Knowledge Based Expert Systems for CAD/CAM Experience of an EDM Application

1993 ◽  
Author(s):  
D. Kiritsis ◽  
Michel Porchet ◽  
L. Boutzev ◽  
I. Zic ◽  
P. Sourdin
Keyword(s):  
Natural Language ◽  
System Development ◽  
Constraint Propagation ◽  
Programming Environment ◽  
Knowledge Based ◽  
Natural Language Programming ◽  
Cad Cam ◽  
The One ◽  
Rule System ◽  
Cam Systems

Abstract In this paper we present our experience from the use of two different expert system development environments to Wire-EDM CAD/CAM knowledge based application. The two systems used follow two different AI approaches: the one is based on the constraint propagation theory and provides a natural language oriented programming environment, while the other is a production rule system with backward-forward chaining mechanisms and a conventional-like programming style. Our experience showed that the natural language programming style offers an easier and more productive environment for knowledge based CAD/CAM systems development.

scholarly journals A New Approach to Creation of an Artificial Intellect and Method of its Implementation

2021 ◽  
Vol 12 (1) ◽  
pp. 87-110
Author(s):  
Wladimir Stalski
Keyword(s):  
Natural Language ◽  
Work Experience ◽  
Humanoid Robot ◽  
Life Experience ◽  
Private Life ◽  
Personal Life ◽  
Human Model ◽  
Self Awareness ◽  
New Approach ◽  
Population Education

Abstract On the basis of the author’s earlier works, the article proposes a new approach to creating an artificial intellect system in a model of a human being that is presented as the unification of an intellectual agent and a humanoid robot (ARb). In accordance with the proposed new approach, the development of an artificial intellect is achieved by teaching a natural language to an ARb, and by its utilization for communication with ARbs and humans, as well as for reflections. A method is proposed for the implementation of the approach. Within the framework of that method, a human model is “brought up” like a child, in a collective of automatons and children, whereupon an ARb must master a natural language and reflection, and possess self-awareness. Agent robots (ARbs) propagate and their population evolves; that is ARbs develop cognitively from generation to generation. ARbs must perform the tasks they were given, such as computing, whereupon they are then assigned time for “private life” for improving their education as well as for searching for partners for propagation. After having received an education, every agent robot may be viewed as a “person” who is capable of activities that contain elements of creativity. The development of ARbs thanks to the evolution of their population, education, and personal “life” experience, including “work” experience, which is mastered in a collective of humans and automatons.

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