Advertisement

Reducing Model of COKB about Operators Knowledge and Solving Problems about Operators

  • Van Nhon Do
  • Hien D. Nguyen
Part of the Studies in Computational Intelligence book series (SCI, volume 572)

Abstract

Knowledge representation plays a very important role for designing knowledge base systems as well as intelligent systems. Nowadays, there are many effective methods for representing such as: semantic network, rule-base systems, computational network. Computational Objects Knowledge Base (COKB) can be used to represent the total knowledge and design the knowledge base of systems. In fact, a popular form of knowledge domain is knowledge about operations and computational relations, especially computational knowledge domain, such as: Linear Algebra, Analytic Geometry. However, COKB model and the other models have not solved yet some problems about operators: specification of operator, properties of operator, reducing an expression. In this paper, we will present a reducing model of COKB. This model, called Ops-model, represents knowledge about operators between objects and solve some problems related to these operators. Through that, the algorithms for designing inference engine of model have been built up. Moreover, Ops-model has been applied to specify a part of knowledge domain about Direct Current (DC) Electrical Circuits and construct a program for solving some problems on this knowledge domain.

Keywords

knowledge representation knowledge-base systems intelligent problem solver automated reasoning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van Harmelem, F., Lifschitz, V., Porter, B.: Bruce: Handbook of Knowledge Representation. Elsevier (2008)Google Scholar
  2. 2.
    Russell, S., Norvig, P.: Artificial Intelligence – A modern approach, 3rd edn. Prentice Hall (2010)Google Scholar
  3. 3.
    Sowa, J.F.: Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks/Cole (2000)Google Scholar
  4. 4.
    Helbig, H.: Knowledge Representation and the Semantics of Natural Language. Springer, Berlin (2006)Google Scholar
  5. 5.
    Tian, Y., Wang, Y., Hu, K.: A Knowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds.) Transactions on Computational Science V. LNCS, vol. 5540, pp. 143–160. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Wang, Y.: On Concept Algebra and Knowledge Representation. In: Proceeding of 5th IEEE International Conference on Cognitive Informatics (ICCI) (2006)Google Scholar
  7. 7.
    Do, N.: Intelligent Problem Solvers in Education: Design Method and Applications. In: Koleshko, V.M. (ed.) Intelligent Systems. InTech (2012)Google Scholar
  8. 8.
    Do, N., Nguyen, H.: A Reasoning method on Knowledge Base of Computational Ojects and Designing a System for automatically solving plane geometry problems. In: Proceeding of World Congress on Engineering and Computer Science 2011 (WCECS 2011), San Francisco, USA, pp. 294–299 (October 2011) ISBN: 978-988-18210-9-6Google Scholar
  9. 9.
    Yang, C., Cai, W.: Knowledge Representations based on Extension Rules. In: Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China (2008)Google Scholar
  10. 10.
    Do, N.V., Nguyen, H.D., Mai, T.T.: Designing an Intelligent Problems Solving System Based on Knowledge about Sample Problems. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS, vol. 7802, pp. 465–475. Springer, Heidelberg (2013)Google Scholar
  11. 11.
    Vietnam Ministry of Education and Training, Textbook and workbook of Physics. Publisher of Education (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Vietnam National University HoChiMinh city (VNU-HCM), University of Information TechnologyHoChiMinh cityVietnam

Personalised recommendations