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Design of Problem Solving Primitives for Efficient Evidential Reasoning

  • Received : 2019.06.03
  • Accepted : 2019.06.14
  • Published : 2019.08.31

Abstract

Efficient evidential reasoning is an important issue in the development of advanced knowledge based systems. Efficiency is closely related to the design of problems solving methods adopted in the system. The explicit modeling of problem-solving structures is suggested for efficient and effective reasoning. It is pointed out that the problem-solving method framework is often too coarse-grained and too abstract to specify the detailed design and implementation of a reasoning system. Therefore, as a key step in developing a new reasoning scheme based on properties of the problem, the problem-solving method framework is expanded by introducing finer grained problem-solving primitives and defining an overall control structure in terms of these primitives. Once the individual components of the control structure are defined in terms of problem solving primitives, the overall control algorithm for the reasoning system can be represented in terms of a finite state diagram.

Keywords

References

  1. R. Greiner, C. Darken, and N.I. Santoso, "Efficient Reasoning," Journal of ACM Computing Surveys, Vol. 33, Iss. 1, pp. 1-30, Mar, 2001. DOI:10.1145/375360.375363.
  2. A. Avron, B. Konikowska, and A. Zamansky, "Efficient Reasoning with Inconsistent Information using C-Systems," Information Sciences, Vol. 296, No. 1, Nov. 2014. DOI:10.10.16/j.ins.2014.11.003
  3. T. KamiM. Knorr, and J. Leite, "Efficient Paraconsistent Reasoning with Ontologies and Rules," Proc. of the 24th Inter. Joint Conference on Artificial Intelligence, pp. 3098-3105, 2015.
  4. K.R. Thorisson, T. Thorarensen, J. Siguroardottir, and B. Steunebrink, "Why Artificial Intelligence Needs a Task Theory," Atrificial General Intelligence, Vol 9782, pp. 118-128, 2016. https://doi.org/10.1007/978-3-319-41649-6_12
  5. E.K. burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and j. Woodward, "A Classification of hyper-heuristic Approaches," Handbook of metaheuristics, pp. 449-468, Aug. 2010. DOI:10.1007/978-1-4419-1665-5_15
  6. K. Munir and M. S. Anjum, "The Use of Ontologies for Effective Knowledge Modelling and Information Retrieval," Applied Computing and Informatics, Vol. 14, Issue 2, pp. 116-126, Jul. 2018. DOI:10.1016/j.aci.2017.07.003
  7. E. Alpaydin, "Machine Learning: The New AI," MIT Press, Sep. 2016.
  8. A Nuxoll and J. Laird, "Enhancing Intelligent Agents with Episodic Memory," Cognitive Systems Research, Vol. 17 pp. 34-48, Jul. 2012. DOI:10.1016/j.cogsys.2011.10.002
  9. L. Chrpa, "Generation of Macro-operators via Investigation of Action Dependencies in Plan," Planning and Scheduling, Vol. 25, Issue 3, pp. 281-297, Sep. 2010. DOI:10.1017/S0269888919999159
  10. T.H. Cho, "Embedding Integglient Planning Capability to DEVS Models by Goal Regression Method," Simulation, Vol. 78 Issue 12, pp. 716-730, Dec. 2002. DOI:10.1177/0037549702078012002
  11. G.S. Lee and I.K. Kim, "A Study on Simplication of Machine Learning Model," Journal of the Institute of Internet, Broadcasting and Communication, Vol. 15, No. 4, pp. 147-152, Aug. 2016, DOI:10.7236/JIIBC.2016.16.5.147