Knowledge extraction using neural network by an artificial life approach

Yuji Makita, Masafurni Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A novel knowledge extraction method from autonomous behavior of multiple mobile robots by an artificial life approach is proposed in this paper. The knowledge is expressed by if-then rules and we employ a neural network for the knowledge extraction. The proposed method has the following features: 1)The structure of knowledge extraction neural network and the learning algorithm are simple; 2)Understanding and modification of the extracted knowledge are easy because weights in the knowledge extraction network directly represent the antecedents and the consequents of the if-then rules; 3)The network itself has an ability of inference using the extracted knowledge. We used a lot of autonomous mobile robots in various environments. Each robot has to avoid the obstacles to get to the goal and the local behavior is extracted and integrated in the knowledge extraction neural network as global knowledge. We confirmed the validity of the proposed method by computer simulations.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers
EditorsXin Yao, Jong-Hwan Kim, Takeshi Furuhashi
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540633995, 9783540633990
Publication statusPublished - 1997
Event1st Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1996 - Taejon, Korea, Republic of
Duration: 1996 Nov 91996 Nov 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1996
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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