Improving the Efficiency of Robot Task Planning by Automatically Integrating Its Planner and Common-Sense Knowledge Base

Author(s):  
Ahmed Al-Moadhen ◽  
Michael Packianather ◽  
Renxi Qiu ◽  
Rossi Setchi ◽  
Ze Ji
2013 ◽  
Vol 22 ◽  
pp. 211-220 ◽  
Author(s):  
Ahmed Al-Moadhen ◽  
Renxi Qiu ◽  
Michael Packianather ◽  
Ze Ji ◽  
Rossi Setchi

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.


Author(s):  
Junia Coutinho Anacleto ◽  
Alexandre Mello Ferreira ◽  
Eliane Nascimento Pereira ◽  
Izaura Maria Carelli ◽  
Marcos Alexandre Rose Silva ◽  
...  

Author(s):  
Abbas Z. Kouzani ◽  
◽  
Fangpo He ◽  
Karl Sammut

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is used as a basic element for the connectionist model. The representation and reasoning ability of the model are described through examples. A common-sense knowledge base is employed to develop a human face detection system. The system consists of three stages: preprocessing, face-components extraction, and final decision making. A neural-network-based algorithm is utilised to extract face components. Five networks are trained to detect the mouth, nose, eyes, and full face. The detected face components and their corresponding possibility degrees enable the knowledge base to locate faces in the image and to generate a membership degree for the detected faces within the face class. The experimental results obtained using this method are presented.


2010 ◽  
Vol 1 (4) ◽  
pp. 10-28 ◽  
Author(s):  
Sarika Jain ◽  
N.K. Jain

EHCPRs system is a knowledge representation and reasoning system for representing common sense knowledge and reasoning with it. In such a system an EHCPR is used as a unit of knowledge for representing any universal concept. There are a number of EHCPRs at various levels of hierarchy of knowledge structure in the EHCPRs system, which results in a tree of EHCPRs. This EHCPRs tree has the capability of continuous growth through new added EHCPRs to it at proper place as well as to get refined continuously with time through improvement in the already acquired EHCPRs. The EHCPRs tree will become stronger in terms of strength of implication and richer in knowledge as time passes. This paper discusses different schemes for enhancing the intelligence, i.e., the knowledge base and the database in the EHCPRs system. By simple and general snippets of code, the EHCPRs system is able to acquire new pieces of knowledge and assimilate it properly in the already acquired knowledge base. The EHCPRs system dynamically restructures the EHCPRs tree in each learning phase by maintaining consistency and minimizing redundancy as well.


1983 ◽  
Vol 6 (5) ◽  
pp. 179-181
Author(s):  
Jim Davies

The arguments of J. Michel are summarised and challenged. Linguistic and political barriers in science and technology are held to be more dependent on group dynamics and mores than on the arguments, traditional to information scientists, mar shalled in the original paper. This paper concludes with a plea for a proper awareness of the cultural, historical background and local 'common sense" knowledge base of the group's initiates, with a perception of current group problems, before adequate information transfer can be effected.


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