Model for Knowledge Representation using Sample Problems and Designing a Program for Automatically Solving Algebraic Problems
Nowadays there are many methods for representing
knowledge such as semantic network, neural network, and conceptual
graphs. Nonetheless, these methods are not sufficiently efficient
when applied to perform and deduce on knowledge domains about
supporting in general education such as algebra, analysis or plane
geometry. This leads to the introduction of computational network
which is a useful tool for representation knowledge base, especially
for computational knowledge, especially knowledge domain about
general education. However, when dealing with a practical problem,
we often do not immediately find a new solution, but we search
related problems which have been solved before and then proposing
an appropriate solution for the problem. Besides that, when finding
related problems, we have to determine whether the result of them
can be used to solve the practical problem or not. In this paper, the
extension model of computational network has been presented. In this
model, Sample Problems, which are related problems, will be used
like the experience of human about practical problem, simulate the
way of human thinking, and give the good solution for the practical
problem faster and more effectively. This extension model is applied
to construct an automatic system for solving algebraic problems in
middle school.
[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] Do Van Nhon, Constructing intelligent systems for computation -
Research and development of knowledge representation models to
design systems for automated solving problems, PhD. thesis, National
University of Ho Chi Minh City (2001-2002).
[4] Hoang Kiem & Do Van Nhon, Extension and development of the
knowledge models of computational objects, Proceedings of National
Conference of some selective problems of Information Technology,
Publisher of Science and Technology (2005).
[5] Do Van Nhon, The architecture of a system for solving problems for
learners and design techniques, Scientific magazine of Education and
Technology, Technical teachers- college of Ho Chi Minh City, No 2(4)
2007.
[6] Nhon Do, An ontology for knowledge representation and Applications,
Proceeding of World Academy of science, engineer and technology, vol.
32, August 2008, ISSN: 2070-370.
[7] Nhon Van Do &Tam Pham Huu, Extensive Computational Networks
And Applying in an Educational Software, Proceedings of 2009
International Conference on Artificial Intelligence and Education
(ICAIE 2009), Wuhan, China, 2009.
[8] Nhon Van Do, Computational Networks for Knowledge Representation,
World Academy of Science, Engineering and Technology, Volume 56,
August 2009, ISSN 2070 - 3724 (ICCSISE 2009), Singapore, 2009.
[9] Vietnam Ministry of Education and Training, Textbook and workbook of
algebra in middle school, Publisher of Education (2006-2007).
[10] George F. Luger & William A Stubblefield, Artificial Intelligence,
Addison Wesley Longman, Inc (1998).
[11] G. Polya. How to solve it, Publisher of Education (1997).
[12] Lakemeyer, G. & Nebel, B. (1994), Foundations of Knowledge
representation and Reasoning. Berlin Heidelberg: Springer-Verlag
(1994).
[13] ToshinoriMunakata, Fundamentals of the New Artificial Intelligence:
Neural, Evolutionary, Fuzzy and More, Springer-Verlag London
Limited (2008).
[14] Frank van Harmelem & Vladimir & Bruce, Handbook of Knowledge
Representation, Elsevier (2008).
[15] http://www.umsolver.com/cgi-bin/club.pl?language=en
[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] Do Van Nhon, Constructing intelligent systems for computation -
Research and development of knowledge representation models to
design systems for automated solving problems, PhD. thesis, National
University of Ho Chi Minh City (2001-2002).
[4] Hoang Kiem & Do Van Nhon, Extension and development of the
knowledge models of computational objects, Proceedings of National
Conference of some selective problems of Information Technology,
Publisher of Science and Technology (2005).
[5] Do Van Nhon, The architecture of a system for solving problems for
learners and design techniques, Scientific magazine of Education and
Technology, Technical teachers- college of Ho Chi Minh City, No 2(4)
2007.
[6] Nhon Do, An ontology for knowledge representation and Applications,
Proceeding of World Academy of science, engineer and technology, vol.
32, August 2008, ISSN: 2070-370.
[7] Nhon Van Do &Tam Pham Huu, Extensive Computational Networks
And Applying in an Educational Software, Proceedings of 2009
International Conference on Artificial Intelligence and Education
(ICAIE 2009), Wuhan, China, 2009.
[8] Nhon Van Do, Computational Networks for Knowledge Representation,
World Academy of Science, Engineering and Technology, Volume 56,
August 2009, ISSN 2070 - 3724 (ICCSISE 2009), Singapore, 2009.
[9] Vietnam Ministry of Education and Training, Textbook and workbook of
algebra in middle school, Publisher of Education (2006-2007).
[10] George F. Luger & William A Stubblefield, Artificial Intelligence,
Addison Wesley Longman, Inc (1998).
[11] G. Polya. How to solve it, Publisher of Education (1997).
[12] Lakemeyer, G. & Nebel, B. (1994), Foundations of Knowledge
representation and Reasoning. Berlin Heidelberg: Springer-Verlag
(1994).
[13] ToshinoriMunakata, Fundamentals of the New Artificial Intelligence:
Neural, Evolutionary, Fuzzy and More, Springer-Verlag London
Limited (2008).
[14] Frank van Harmelem & Vladimir & Bruce, Handbook of Knowledge
Representation, Elsevier (2008).
[15] http://www.umsolver.com/cgi-bin/club.pl?language=en
@article{"International Journal of Engineering, Mathematical and Physical Sciences:61036", author = "Nhon Do and Hien Nguyen", title = "Model for Knowledge Representation using Sample Problems and Designing a Program for Automatically Solving Algebraic Problems", abstract = "Nowadays there are many methods for representing
knowledge such as semantic network, neural network, and conceptual
graphs. Nonetheless, these methods are not sufficiently efficient
when applied to perform and deduce on knowledge domains about
supporting in general education such as algebra, analysis or plane
geometry. This leads to the introduction of computational network
which is a useful tool for representation knowledge base, especially
for computational knowledge, especially knowledge domain about
general education. However, when dealing with a practical problem,
we often do not immediately find a new solution, but we search
related problems which have been solved before and then proposing
an appropriate solution for the problem. Besides that, when finding
related problems, we have to determine whether the result of them
can be used to solve the practical problem or not. In this paper, the
extension model of computational network has been presented. In this
model, Sample Problems, which are related problems, will be used
like the experience of human about practical problem, simulate the
way of human thinking, and give the good solution for the practical
problem faster and more effectively. This extension model is applied
to construct an automatic system for solving algebraic problems in
middle school.", keywords = "educational software, artificial intelligence,knowledge base system, knowledge representation.", volume = "4", number = "6", pages = "708-6", }