Research of Maize Field Knowledge Representation Building Based on Ontology

2012 ◽  
Vol 546-547 ◽  
pp. 441-445
Author(s):  
Ying Zhang ◽  
Gui Fen Chen
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Semantic Level ◽  
Maize Field ◽  
Complex Knowledge ◽  
The Relationship ◽  
Knowledge Ontology ◽  
Knowledge Hierarchy

The knowledge representation of the traditional artificial intelligence used different modeling methods and the different development tools, it led to the lack of interoperability between all kinds of knowledge, ontology solved the problem. Ontology, which is a model in semantic and knowledge hierarchy describing the concept and the relationship between the concepts, has been the focus of the field of artificial intelligence since it was proposed. This paper explored the knowledge representation based on ontology in the field of artificial intelligence, built the maize domain knowledge ontology, the result shows: ontology can effectively solve the heterogeneous problem of expression of complex knowledge, makes the computer to understand information for the semantic level, and benefit to develop the intelligent systems of maize.

Quality of Knowledge in Virtual Entities

2008 ◽  
pp. 1360-1367
Author(s):  
Cesar Analide ◽  
Paulo Novais ◽  
José Machado ◽  
José Neves
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Intelligent Systems ◽  
Mental States ◽  
Multi Agent Systems ◽  
Imperfect Knowledge ◽  
Multi Agent ◽  
Extended Logic Programming ◽  
Work Done

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

scholarly journals OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 125
Author(s):  
Maria A. Cornejo-Lupa ◽  
Yudith Cardinale ◽  
Regina Ticona-Herrera ◽  
Dennis Barrios-Aranibar ◽  
Manoel Andrade ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Autonomous Robots ◽  
State Of The Art ◽  
Formal Representation ◽  
Robot Operating System ◽  
Localization And Mapping ◽  
Complex Knowledge ◽  
Structural Levels ◽  
Mapping Information

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

scholarly journals Artificial intelligence as a technology of the future at the present stage of development of society

2021 ◽  
Vol 7 (Extra-D) ◽  
pp. 391-397
Author(s):  
Maksym Iasechko ◽  
Mykhailo Kharlamov ◽  
Hanna Skrypchuk ◽  
Kateryna Fadyeyeva ◽  
Lіydmyla Gontarenko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Computer Technology ◽  
Intelligent Systems ◽  
Present Stage ◽  
Stage Of Development ◽  
Intellectual System ◽  
The Future ◽  
And Training

The article establishes that the vector of research in the field of artificial intelligence is aimed at developing methods of formalization, generalization, classification, knowledge representation; study and formalization of reasoning, their modeling; research of communication, the specifics of the dialogue between the intellectual system and the person; development of algorithms for the operation of computer technology and training of intelligent systems.

Quality of Knowledge in Virtual Entities

2011 ◽  
pp. 188-194
Author(s):  
Cesar Analide ◽  
Paulo Novais ◽  
José Machado ◽  
José Neves
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Intelligent Systems ◽  
Mental States ◽  
Multi Agent Systems ◽  
Imperfect Knowledge ◽  
Multi Agent ◽  
Extended Logic Programming ◽  
Work Done

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

Quality of Knowledge in Virtual Entities

2011 ◽  
pp. 436-442 ◽  
Author(s):  
Cesar Analide ◽  
Paulo Novais ◽  
Jose Machado ◽  
Jose Neves
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Intelligent Systems ◽  
Mental States ◽  
Multi Agent Systems ◽  
Imperfect Knowledge ◽  
Multi Agent ◽  
Extended Logic Programming ◽  
Work Done

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

A Model of Hierarchical Knowledge Representation – Toward Knowware for Intelligent Systems

2007 ◽  
Vol 11 (10) ◽  
pp. 1232-1240 ◽  
Author(s):  
Liya Ding ◽  
Keyword(s):  
Knowledge Representation ◽  
Intelligent Systems ◽  
Automatic Construction ◽  
Knowledge Hierarchy

We propose a model for multiresolutionary knowledge representation; define concepts of domain, application, and working hierarchies; and discuss inference mechanisms in the knowledge hierarchy. We also introduce an automatic construction of the knowledge hierarchy for the development of intelligent systems.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

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