Knowledge acquisition from parsing natural language expressions for humanoid robot action commands

2020 ◽  
Vol 57 (6) ◽  
pp. 102094
Author(s):  
Diego Reforgiato Recupero ◽  
Federico Spiga
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Humanoid Robot

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

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.

scholarly journals PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

2019 ◽  
Vol 9 (18) ◽  
pp. 3789 ◽  
Author(s):  
Jiyoun Moon ◽  
Beom-Hee Lee
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Scene Understanding ◽  
Semantic Knowledge ◽  
Ground Vehicles ◽  
Learning Techniques ◽  
Recent Developments ◽  
Heterogeneous Robots ◽  
Semantic Scene

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.

Putting ontologies to use

1998 ◽  
Vol 13 (1) ◽  
pp. 1-3 ◽  
Author(s):  
MIKE USCHOLD ◽  
AUSTIN TATE
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Language Processing ◽  
Common Core ◽  
Process Management ◽  
Planning Process ◽  
Schema Integration ◽  
Nature Development ◽  
Theoretical Issues

Interest in the nature, development and use of ontologies is becoming increasingly widespread. Since the early nineties, numerous workshops have been held. Representatives from historically separate disciplines concerned with philosophical issues, knowledge acquisition and representation, planning, process management, database schema integration, natural language processing and enterprise modelling, came together to identify a common core of issues of interest. There was highly varied and inconsistent usage of a wide variety of terms, most notably, “ontology”, rendering cross-discipline communication difficult. However, progress was made toward understanding the commonality among the disciplines. Subsequent workshops addressed various aspects of the field, including theoretical issues, methodologies for building ontologies, as well as specific applications in government and industry.

A PROLOG-BASED CHINESE EXPERT SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 17-24
Author(s):  
HSU LOKE SOO
Keyword(s):  
Expert System ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Domain Expert ◽  
Knowledge Engineer ◽  
Knowledge Application ◽  
Design And Implementation ◽  
Expert System Shell ◽  
Natural Language Interface ◽  
Application Module

This paper presents the design and implementation of a Chinese Expert System Shell which is based on a Chinese Prolog interpreter. The system is divided into three parts: the knowledge acquisition module, the knowledge application module and the inference engine. The knowledge engineer defines the syntax of the language to be used by himself and by the users when they interact with the system. The natural language interface is table driven and can be modified easily. The system also caters for the case when the domain expert finds it difficult to articulate the rules, but is able to give examples. An inductive engine is included to extract rules from examples.

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