Commenced in January 2007
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Edition: International
Paper Count: 31106
Computational Networks for Knowledge Representation

Authors: Nhon Van Do


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, Knowledge Engineering, Knowledge Representation, artificial intelligence and education

Digital Object Identifier (DOI):

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[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.