• DocumentCode
    3229083
  • Title

    Using logic rules for concept refinement learning in first order logic

  • Author

    Shi, Zhenguo ; Liu, Zongtian ; Chen, Jianping

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Nantong Univ., Nantong, China
  • fYear
    2010
  • fDate
    23-26 Sept. 2010
  • Firstpage
    444
  • Lastpage
    448
  • Abstract
    In this paper, it has been explored that the use of logic rules as key element in concept refinement Learning. A logic rule is a formal grammar in logic for expressing formation rules of a formal language. First order logic in Inductive Logic Programming(ILP) and programming language in Genetic Programming(GP) are formal languages, the logic rule is available to express syntax and semantics of them. Concept refinement learning including inductive concept learning by employing ILP and evolutionary concept learning by employing GP.A framework is presented that combining ILP and GP using logic rules for concept refinement learning in first order logic. The viability of our approach is illustrated by comparing the performance of our learner with that of other concept learners such as Progol, CfgGP, GGP on a variety of target concepts. We conclude with some observations about the merits of our approach and about possible extensions.
  • Keywords
    formal languages; grammars; inductive logic programming; learning by example; programming languages; evolutionary concept learning; first order logic; formal grammar; formal language; formal languages; genetic programming; inductive concept refinement learning; inductive logic programming; logic rules; programming language; Genetics; evolutionary concept learning; genetic programming; inductive concept learning; inductive logic programming; logic rule; refinement concept learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-6437-1
  • Type

    conf

  • DOI
    10.1109/BICTA.2010.5645166
  • Filename
    5645166