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Hybrid Behavior Evolution Model Using Rule and Link Descriptors
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Sa Joon Park (School of Mobile Contents, Daegu Haany University)
Vol. 12, No. 3, Page: 67 ~ 82
Keywords
Behavior Evolution Model, Rule Descriptor, Link Descriptor
Abstract
We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.
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규칙 구성자와 연결 구성자를 이용한 혼합형 행동 진화 모델
박사준 (대구한의대학교 모바일콘텐츠학부)
Abstract
가상 로봇의 행동 진화를 위해서 규칙 구성자와 연결 구성자를 구성하여 분류 규칙과 진화 신경망을 형성하는 혼합형 행동 진화 모델(Hybrid Behavior Evolution Model)을 제안한다. 본 모델에서는 행동 지식을 두 수준에서 표현하였다. 상위 수준에서는 규칙 구성자와 연결 구성자를 구성하여 표현력을 향상시켰다. 하위 수준에서는 행동 지식을 비트 스트링 형태의 염색체로 표현하여, 이들 염색체를 대상으로 유전자 연산을 적용하여 학습을 수행시켰다. 적합도가 최적인 염색체를 추출하여 가상 로봇을 구성하였다. 구성된 가상 로봇은 주변 상황을 인식하여 입력 정보와 규칙 정보를 이용하여 패턴을 분류하였고, 그 결과를 신경망에서 처리하여 행동하였다. 제안된 모델을 평가하기 위해서 HBES(Hybrid Behavior Evolution System)를 개발하여 가상 로봇의 먹이 수집 문제에 적용하였다. 제안한 시스템을 실험한 결과, 동일한 조건의 진화 신경망보다 학습 시간이 적게 소요되었다. 그리고, 규칙이 적합도 향상에 주는 영향을 평가하기 위해서, 학습이 완료된 염색체들에 대해서 규칙을 적용한 것과, 그렇지 않은 것을 각각 수행하여 적합도를 측정하였다. 그 결과, 규칙을 적용하지 않으면 적합도가 저하되는 것을 확인하였다. 제안된 모델은 가상 로봇의 행동 진화에 있어서 기존의 진화 신경망 방식 보다 학습 성능이 우수하고 규칙적인 행동을 수행하는 것을 확인하였다.
Cite this article
JIIS Style
Park, S. J., , "Hybrid Behavior Evolution Model Using Rule and Link Descriptors", Journal of Intelligence and Information Systems, Vol. 12, No. 3 (2006), 67~82.

IEEE Style
Sa Joon Park, "Hybrid Behavior Evolution Model Using Rule and Link Descriptors", Journal of Intelligence and Information Systems, vol. 12, no. 3, pp. 67~82, 2006.

ACM Style
Park, S. J.,, 2006. Hybrid Behavior Evolution Model Using Rule and Link Descriptors. Journal of Intelligence and Information Systems. 12, 3, 67--82.
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@article{Park:JIIS:2006:269,
author = {Park, Sa Joon},
title = {Hybrid Behavior Evolution Model Using Rule and Link Descriptors},
journal = {Journal of Intelligence and Information Systems},
issue_date = {September 2006},
volume = {12},
number = {3},
month = Sep,
year = {2006},
issn = {2288-4866},
pages = {67--82},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Behavior Evolution Model, Rule Descriptor and Link Descriptor },
}
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%A Sa Joon Park
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%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 12
%N 3
%P 67-82
%D 2006
%R
%I Korea Intelligent Information System Society