An adaptive Inductive Logic Programming system using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{wong:1995:ilpGP,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "An adaptive Inductive Logic Programming system using
                 Genetic Programming",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and 
                 David B. Fogel",
  pages =        "737--752",
  publisher_address = "Cambridge, MA, USA",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming, Uncle
                 problem",
  ISBN =         "0-262-13317-2",
  URL =          "http://cptra.ln.edu.hk/~mlwong/conference/ep1995.pdf",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300813",
  size =         "16 pages",
  abstract =     "Recently, there have been increasing interests in
                 Inductive Logic Programming (ILP) systems. But existing
                 ILP systems cannot improve themselves automatically.
                 This paper describes an Adaptive Inductive Logic
                 Programming (Adaptive ILP) system that evolves during
                 learning. An adaptive ILP system is composed of an
                 external interface, a biases base, a knowledge base of
                 background knowledge, an example database, an empirical
                 ILP learner, a meta-level learner, and a learning
                 controller. A preliminary adaptive ILP system has been
                 implemented. In this implementation, the empirical ILP
                 learner performs top-down search in the hypothesis
                 space defined by the concept description language, the
                 language bias, and the background knowledge. The search
                 is directed by search biases which can be induced and
                 refined by genetic programming (Koza 1992).

                 It has been demonstrated that the adaptive ILP system
                 performs better than FOIL, a famous ILP system (Quinlan
                 1990), in inducing logic programs from perfect or noisy
                 training examples. The experimentation illustrates the
                 benefit of an adaptive ILP system over existing ILP
                 systems. The result implies that the search bias
                 induced by genetic programming (GP) is better than that
                 of FOIL, which is designed by a top researcher in the
                 field. Consequently, GP is a promising technique for
                 implementing a meta-level learning system. The result
                 is very encouraging as it suggests that the process of
                 natural selection and evolution can successfully evolve
                 a high performance ILP system.",
  notes =        "EP-95",
}

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung

Citations