An adaptive knowledge-acquisition system using generic genetic programming

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)47 - 58
Number of pages12
JournalExpert Systems with Applications
Volume15
Issue number1
DOIs
Publication statusPublished - Jul 1998
Externally publishedYes

Fingerprint

Genetic programming
Knowledge acquisition
Learning systems
Inductive logic programming (ILP)
Knowledge based systems
Controllers

Cite this

@article{412de60776f7482f8db57bc38ae6d903,
title = "An adaptive knowledge-acquisition system using generic genetic programming",
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.",
author = "WONG, {Man Leung}",
year = "1998",
month = "7",
doi = "10.1016/S0957-4174(98)00010-4",
language = "English",
volume = "15",
pages = "47 -- 58",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "1",

}

An adaptive knowledge-acquisition system using generic genetic programming. / WONG, Man Leung.

In: Expert Systems with Applications, Vol. 15, No. 1, 07.1998, p. 47 - 58.

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

TY - JOUR

T1 - An adaptive knowledge-acquisition system using generic genetic programming

AU - WONG, Man Leung

PY - 1998/7

Y1 - 1998/7

N2 - 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.

AB - 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.

UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-0003922897&doi=10.1016%2fS0957-4174%2898%2900010-4&partnerID=40&md5=b3cf92ebbf417aab34482936b70f0583

U2 - 10.1016/S0957-4174(98)00010-4

DO - 10.1016/S0957-4174(98)00010-4

M3 - Journal Article (refereed)

VL - 15

SP - 47

EP - 58

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 1

ER -