Computational Networks for Knowledge Representation
In the artificial intelligence field, knowledge
representation and reasoning are important areas for intelligent
systems, especially knowledge base systems and expert systems.
Knowledge representation Methods has an important role in
designing the systems. There have been many models for knowledge
such as semantic networks, conceptual graphs, and neural networks.
These models are useful tools to design intelligent systems. However,
they are not suitable to represent knowledge in the domains of reality
applications. In this paper, new models for knowledge representation
called computational networks will be presented. They have been
used in designing some knowledge base systems in education for
solving problems such as the system that supports studying
knowledge and solving analytic geometry problems, the program for
studying and solving problems in Plane Geometry, the program for
solving problems about alternating current in physics.
[1] Stuart Russell & Peter Norvig, Artificial Intelligence - A modern
approach (second edition), Prentice Hall, 2003.
[2] John F. Sowa. Knowledge Representation: Logical, Philosophical and
Computational Foundations, Brooks/Cole, 2000
[3] George F. Luger, Artificial Intelligence: Structures And Strategies For
Complex Problem Solving, Addison Wesley Longman, 2008.
[4] Chitta Baral, Knowledge Representation, Reasoning and Declarative
Problem Solving, Cambridge University Press, 2003.
[5] Do Van Nhon, "A Program for studying and Solving problems in Plane
Geometry", in Proc. Conf. on Artificial Intelligence 2000, Las Vegas,
USA, 2000, pp. 1441-1447.
[6] Do Van Nhon, "A system that supports studying knowledge and solving
of analytic geometry problems", in Proc. 16th World Computer
Congress 2000 conf. on Education Uses of Information and
Communication Technologies, Beijing, China, 2000, pp. 236-239.
[7] Nhon Do, An ontology for knowledge representation And Applications.
Waset, International Conference on Data, Information and Knowledge
Management, Singapore, 2008.
[8] Michel Chein & Marie-Laure Mugnier, Graph-based Knowledge
representation: Computational foundations of Conceptual Graphs,
Springer-Verlag London Limited 2009.
[9] Frank van Harmelem & Vladimir & Bruce, Handbook of Knowledge
Representation, Elsevier, 2008.
[10] F. Lehmann, Semantic Networks in Artificial Intelligence, Elsevier
Science Ltd, 2008.
[11] Amit Konar, Computational Intelligence : Principles, Techniques and
Applications, Springer-Verlag Berlin Heidelberg, 2005.
[12] Leszek Rutkowski, Computational Intelligence: Methods and
Techniques, Springer-Verlag Berlin Heidelberg, 2008.
[13] ToshinoriMunakata, Fundamentals of the New Artificial Intelligence:
Neural, Evolutionary, Fuzzy and More, Springer-Verlag London
Limited, 2008.
[14] M. Tim Jones, Artificial Intelligence : A System Approach, Infinity
Science Press LLC, 2008.
[15] Nhon Do & Tuyen Tran T. & Phan Truong H., Design method for
Knowledge Base Systems in Education using COKB-ONT. Waset,
International Conference on Communication and Information
technologies in Education, Thailand, 2008.
[1] Stuart Russell & Peter Norvig, Artificial Intelligence - A modern
approach (second edition), Prentice Hall, 2003.
[2] John F. Sowa. Knowledge Representation: Logical, Philosophical and
Computational Foundations, Brooks/Cole, 2000
[3] George F. Luger, Artificial Intelligence: Structures And Strategies For
Complex Problem Solving, Addison Wesley Longman, 2008.
[4] Chitta Baral, Knowledge Representation, Reasoning and Declarative
Problem Solving, Cambridge University Press, 2003.
[5] Do Van Nhon, "A Program for studying and Solving problems in Plane
Geometry", in Proc. Conf. on Artificial Intelligence 2000, Las Vegas,
USA, 2000, pp. 1441-1447.
[6] Do Van Nhon, "A system that supports studying knowledge and solving
of analytic geometry problems", in Proc. 16th World Computer
Congress 2000 conf. on Education Uses of Information and
Communication Technologies, Beijing, China, 2000, pp. 236-239.
[7] Nhon Do, An ontology for knowledge representation And Applications.
Waset, International Conference on Data, Information and Knowledge
Management, Singapore, 2008.
[8] Michel Chein & Marie-Laure Mugnier, Graph-based Knowledge
representation: Computational foundations of Conceptual Graphs,
Springer-Verlag London Limited 2009.
[9] Frank van Harmelem & Vladimir & Bruce, Handbook of Knowledge
Representation, Elsevier, 2008.
[10] F. Lehmann, Semantic Networks in Artificial Intelligence, Elsevier
Science Ltd, 2008.
[11] Amit Konar, Computational Intelligence : Principles, Techniques and
Applications, Springer-Verlag Berlin Heidelberg, 2005.
[12] Leszek Rutkowski, Computational Intelligence: Methods and
Techniques, Springer-Verlag Berlin Heidelberg, 2008.
[13] ToshinoriMunakata, Fundamentals of the New Artificial Intelligence:
Neural, Evolutionary, Fuzzy and More, Springer-Verlag London
Limited, 2008.
[14] M. Tim Jones, Artificial Intelligence : A System Approach, Infinity
Science Press LLC, 2008.
[15] Nhon Do & Tuyen Tran T. & Phan Truong H., Design method for
Knowledge Base Systems in Education using COKB-ONT. Waset,
International Conference on Communication and Information
technologies in Education, Thailand, 2008.
@article{"International Journal of Information, Control and Computer Sciences:59114", author = "Nhon Van Do", title = "Computational Networks for Knowledge Representation", abstract = "In the artificial intelligence field, knowledge
representation and reasoning are important areas for intelligent
systems, especially knowledge base systems and expert systems.
Knowledge representation Methods has an important role in
designing the systems. There have been many models for knowledge
such as semantic networks, conceptual graphs, and neural networks.
These models are useful tools to design intelligent systems. However,
they are not suitable to represent knowledge in the domains of reality
applications. In this paper, new models for knowledge representation
called computational networks will be presented. They have been
used in designing some knowledge base systems in education for
solving problems such as the system that supports studying
knowledge and solving analytic geometry problems, the program for
studying and solving problems in Plane Geometry, the program for
solving problems about alternating current in physics.", keywords = "Artificial intelligence, artificial intelligence and
education, knowledge engineering, knowledge representation.", volume = "3", number = "8", pages = "2049-5", }