- author = "Ryutaro Ichise and Masayuki Numao",
- title = "Inductive Learning with Inductive Logic Programming and Genetic Programming",
- journal = "Journal of Japanese Society for Artificial Intelligence",
- year = "1999",
- volume = "14",
- number = "2",
- pages = "307--314",
- month = mar,
- keywords = "genetic algorithms, genetic programming, ILP",
- ISSN = "0912-8085",
- broken = "http://sciencelinks.jp/j-east/article/199911/000019991199A0325870.php",
-
URL = "
http://www.ai-gakkai.or.jp/en/vol14_no2/",
- abstract = "Two approaches to inducing a concept represented in first order logic are inductive logic programming(ILP) and genetic programming(GP). In ILP, concept learning can be considered as a search in the space specified by the background knowledge, and in which the goal concept is represented by Horn clauses. On the other hand, in GP, the search space is specified by terminal and nonterminal symbols, and the goal is represented generally by S-expressions. These two approaches are very similar in terms of their methods and goals, yet their combination in previous work is rare. In this paper, we propose a method that synthesises the inductive logic programming and genetic programming approaches. The concept behind this approach is to combine the search method of GP, that is, Genetic Algorithm, with the type and mode methods of ILP. We have implemented a system called SYNGIP (SYNthesized system with Genetic programming and Inductive logic Programming) based on the method. Experimental results show that the proposed method can be used to treat, in the same way, learning from training examples that do not have discrete classes, and learning from both positive and negative training examples. Moreover, the proposed method constitutes a novel solution to the closure problem and provides a new bias for concept learning. (author abst.)",
-
notes = "Language=Japanese Journal Code=X0330A Accession
number=99A0325870
Tokyo Inst. of Technology, Graduate School",
