- author = "Man Leung Wong",
- title = "An adaptive knowledge-acquisition system using generic genetic programming",
- journal = "Expert Systems with Applications",
- volume = "15",
- pages = "47--58",
- year = "1998",
- number = "1",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "0957-4174",
-
DOI = "
doi:10.1016/S0957-4174(98)00010-4",
-
URL = "
http://cptra.ln.edu.hk/~mlwong/journal/esa1998.pdf",
-
URL = "
http://www.sciencedirect.com/science/article/B6V03-3TGSH84-3/1/83b0941e6fae053fea766e293d408cf9",
- size = "12 pages",
-
abstract = "The knowledge-acquisition bottleneck greatly obstructs
the development of knowledge-based systems. One popular
approach to knowledge acquisition uses inductive
concept learning to derive knowledge from examples
stored in databases. However, existing learning systems
cannot improve themselves automatically. This paper
describes an adaptive knowledge-acquisition system that
can learn first-order logical relations and improve
itself automatically. The 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. In this system, 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 the meta-level learner based on generic
genetic programming.
It has been demonstrated that the adaptive knowledge-acquisition system performs better than FOIL on inducing logical relations from perfect or noisy training examples. The result implies that the search bias evolved by evolutionary learning is better than that of FOIL which is designed by a top researcher in the field. Consequently, generic genetic programming 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 learning system.",
