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Learning techniques in Extended Hierarchical Censored Production Rules (EHCPRs) System

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Abstract

EHCPRs system is an intelligent system for knowledge representation and reasoning with all the possible learning methods incorporated in it. In such a system an EHCPR is used as a unit of knowledge for representing any concept independent of its application domain. There are a number of EHCPRs at various levels of hierarchy connected to each other via a number of operators, hence resulting in a knowledge structure of hierarchical network of EHCPRs. This EHCPRs network has the capability of continuous expansion through new added EHCPRs at proper place of the hierarchy as well as these already acquired EHCPRs are subject to continuous refinement with time. The EHCPRs tree in the network will become stronger in terms of strength of implication and richer in knowledge with time. This paper discusses different learning schemes possible for enhancing the knowledge base and the database of the EHCPRs system. The EHCPRs System is made to learn so that it is able to serve the multilingual global community. The online EHCPRs system will open all new avenues of learning methods in the system.

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References

  • Bharadwaj KK, Jain NK (1992) Hierarchical censored production rules (HCPRs) system. Data Knowl Eng, North Holland 8: 19–34

    Google Scholar 

  • Bharadwaj KK, Kandwal R. (2005) Cumulative learning based on dynamic clustering of hierarchical production rules (HPRs). Proceedings of 6th international conference on machine intelligence, ICMI, Budapest, Hungary, pp 333–338

  • Bharadwaj KK, Kandwal Rekha (2008) Cumulative learning techniques in production rules with fuzzy hierarchy (PRFH) system. J Exp Theor Artif Intell 20(2): 111–132

    Article  Google Scholar 

  • Chaudhary D, Jain S, Jain NK (2011) Globalization of EHCPRs System. Proceeding of 7th international conference on upcoming trends in IT-2011 (ICUTIT-2011), 26 March

  • Jain NK, Bharadwaj KK (1998) Some learning techniques in hierarchical censored production rules (HCPRs) system. Int J Intell Syst, vol 13, pp 319–344, Wiley, North Holland

  • Jain NK, Bharadwaj KK, Marranghello N (1999) Extended hierarchical censored production rules (EHCPRs) system: an approach towards generalized knowledge representation. J Intell Syst, vol 9, pp 259–295, Freund & Pettman, UK

  • Jain NK, Jain S, Goel CK (2007) A generalized knowledge representation system and its implementation: an extended hierarchical censored production rules system (EHCPRs). MERI, J Manag IT 1: 88–101

    Google Scholar 

  • Jain S, Jain NK, Goel CK (2008a) Implementing general control scheme in EHCPRs system. Proceedings of second national conference on mathematical techniques: emerging paradigms for electronics and IT industries (MATEIT-2008), pp 347–353

  • Jain S, Jain NK, Goel CK (2008b) EHCPRs system and management. JIPEM 3(1): 1–17

    MATH  Google Scholar 

  • Jain S, Jain NK, Goel CK (2009a) Reasoning in EHCPRs system. Int J Open Problems Comput Math 2(2): 173–193

    MathSciNet  Google Scholar 

  • Jain S, Jain NK (2009b) A generalized knowledge representation system for context sensitive reasoning: generalized HCPRs system. Artif Intell Rev 30(1): 39–52. doi:10.1007/s10462-009-9115-8

    Article  Google Scholar 

  • Jain S, Jain NK (2009c) Representation of defaults and constraints in EHCPRs system: an implementation. Int J Adapt Innov Syst 1(2): 105–120

    Google Scholar 

  • Jain S, Jain NK (2010) Scope of natural language translations in EHCPRs System. Proceedings of international conference on role of translation in nation building, nationalism and supra-nationalism, 16–19 Dec

  • Jain S, Chaudhary D, Jain NK (2011) Localization of EHCPRs System in the multilingual domain: an implementation. Inform Syst Indian Lang Int Conf (ICISIL) 139(3): 314–316. doi:10.1007/978-3-642-19403-0_61

    Article  Google Scholar 

  • Kandwal R, Bharadwaj KK (2005) A cumulative learning approach to data mining employing censored production rules (CPRs). Proceedings Of 5th international Enformatika conference on computational intelligence (ICCI-2005), Prague, pp 294–298

  • Kandwal R, Bharadwaj KK (2006) Cumulative growth of production rules with fuzzy hierarchy (PRFH). Proc of 10th IASTED conference on artificial intelligence and soft computing, ASC-2006, pp 40–45

  • Kandwal R, Bharadwaj KK (2007) Censor updation during dynamic clustering of hierarchical censored production rules (HCPRs). Fourth international conference on fuzzy systems and knowledge discovery, FSKD-2007, pp 224–229

  • Kubat M, Bratko I, Michalski RS (1998) A review of machine learning methods. In: Michalski RS, Bratko I, Kubat M (eds) Machine learning and data mining: methods and applications. Wiley, London, pp 3–69

    Google Scholar 

  • McCarthy J (1987) Generality in artificial intelligence. Commun ACM 30(12): 1030–1035

    Article  MathSciNet  MATH  Google Scholar 

  • McDermott D, Doyle J (1980) Nonmonotonic logic I. Artif Intell 13: 41–75

    Article  MathSciNet  MATH  Google Scholar 

  • Michalski RS (2003) Inferential theory of learning and inductive databases. Presented at the UQÀM summer institute in cognitive sciences, Montreal, June 30th–July 11th

  • Michalski RS, Winston PH (1986) Variable precision logic. Artif Intell, 29, pp 121–145, North Holland

    Google Scholar 

  • Mitchell T (1997) Machine learning. McGraw-Hill Companies, Inc., NY

    MATH  Google Scholar 

  • Newell A, Simon HA (1972) Human problem solving. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Plato (1952) The Dialogues of Plato (trans: Jowett B). Great Books of the Western World, Encyclopedia Britannica, Inc., Chicago

  • Reiter R (1980) A logic for default reasoning. Artif Intell J 13: 81–132

    Article  MathSciNet  MATH  Google Scholar 

  • Zhou HH (1990) CSM: a computational model of cumulative learning. Mach Learn 5: 383–406

    Google Scholar 

Download references

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Correspondence to Sarika Jain.

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Jain, S., Jain, N.K. Learning techniques in Extended Hierarchical Censored Production Rules (EHCPRs) System. Artif Intell Rev 38, 97–117 (2012). https://doi.org/10.1007/s10462-011-9242-x

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