{"id":1000335,"date":"2003-09-01T10:47:40","date_gmt":"2003-09-01T01:47:40","guid":{"rendered":"http:\/\/nijiiro-farm.sakura.ne.jp\/AI_T\/?p=335"},"modified":"2021-05-18T19:36:27","modified_gmt":"2021-05-18T10:36:27","slug":"vol18_no5","status":"publish","type":"page","link":"https:\/\/www.ai-gakkai.or.jp\/en\/published_books\/journals_of_jsai\/past_journals\/in2003\/vol18_no5\/","title":{"rendered":"Journal of the Japanese Society for Artificial Intelligence Vol.18 No.5(Sep. 2003)"},"content":{"rendered":"<p>CONTENTS<\/p>\n<p>Special Issue:\u201cRecent Progress in Genetic Algorithms \u201d<br \/>\nEditors\u2019 Introduction to \u201cRecent Progress in Genetic Algorithms \u201d<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Masayuki Yamamura, Hajime Kita and Isao Ono 477<\/p>\n<p>Advances in Real-Coded Genetic Algorithm Considering Probablistic<br \/>\nDistribution Estimation \u2026\u2026\u2026\u2026\u2026.. Jun Sakuma and Shigenobu Kobayashi 479<\/p>\n<p>Perspective on Estimation of Distribution Algorithms<br \/>\n\u2014 Bayesian Optimization Algorithm and Its Hybridization \u2014<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Setsuya Kurahashi, Yuji Katsumata and Takao Terano 487<\/p>\n<p>Multiobjective Design Optimization of Aircraft Configuration<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Shigeru Obayashi 495<\/p>\n<p>Application of Real-Coded Genetic Algorithm to Biological Research Fields<br \/>\n\u2014 Inference of Genetic Interactions in Large Scale Gene Networks \u2014<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Masahiro Okamoto and Isao Ono 502<\/p>\n<p>Optimization of Uncertain Fitness Functions by Genetic Algorithms<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Hajime Kita and Yasuhito Sano 510<\/p>\n<p>Special Issue:<br \/>\n\u201cMachine Learning vs. Human Learning: Why Can\u2019t Machines Outsmart Humans ?\u201d<br \/>\nEditor\u2019s Introduction to<br \/>\n\u201cMachine Learning vs. Human Learning: Why Can\u2019t Machines Outsmart Humans ?\u201d<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. Mutsumi Imai 517<\/p>\n<p>Various Approaches to Machine Learning<br \/>\n\u2014 Researchers\u2019Map \u2014 \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Hiroshi Yamakawa 519<\/p>\n<p>Machine Learning for Language Communication \u2026\u2026\u2026\u2026\u2026.. Naoto Iwahashi 522<\/p>\n<p>Machine Learning and Human Learning<br \/>\n\u2014 From the Viewpoint of the Statistical Learning \u2014 \u2026\u2026.. Hideki Asoh 526<\/p>\n<p>Machine Learning in Computational Learning Theory \u2026\u2026\u2026.. Hiroki Arimura 531<\/p>\n<p>Can a Machine Produce Appropriate New Concepts ? \u2026\u2026\u2026. Makoto Haraguchi 537<\/p>\n<p>Modeling of Children\u2019s Language Acquisition by Inductive Logic Programming<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. Koichi Furukawa 542<\/p>\n<p>Logical Approach to AI Using Learning \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Ken Satoh 546<\/p>\n<p>Learning in Cognitive Develepmental Robotics \u2026\u2026\u2026\u2026\u2026\u2026 Minoru Asada 550<\/p>\n<p>Can We Make Human Intelligence Clear by the Computational Model ?<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Hiroyuki Okada 555<\/p>\n<p>Acquisition of Internal Procedure by Reuse of Knowledge and Experience<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Takashi Omori 559<\/p>\n<p>Toward a System That Learns Something Forever \u2026\u2026\u2026\u2026 Hitoshi Matsubara 564<\/p>\n<p>Multiple Processes for Decision-Making \u2026\u2026\u2026\u2026\u2026\u2026 Masamichi Sakagami 568<\/p>\n<p>Flexibility of Human Intelligence: What Machine Learning Suggests<br \/>\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. Etsuko Haryu 572<\/p>\n<p>Human and Machine Learning at Home \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Hisao Nojima 577<\/p>\n<p>Conditions for Language Learning to Be Symbol-Grounded \u2026\u2026.. Mutsumi Imai 580<\/p>\n<p>Survey Papers<br \/>\nEfficient Construction and Use of Enumeration Algorithms (Part 3)<br \/>\n\u2014 Hard Enumeration Problems and Reverse Search \u2014 \u2026\u2026\u2026. Takeaki Uno 586<\/p>\n<p>Lecture Series: New Trends in Linguistics for AI Reseachers to Learn From (2)<br \/>\nRelevance Theory<br \/>\n\u2014 An Introduction to Cognitive Pragmatics \u2014 \u2026\u2026\u2026.. Tomoko Matsui 592<\/p>\n<p>Technical Papers Abstract \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 603<\/p>\n<p>Last modified: Mon. Jul. 07 15:45:45 JST 2003<\/p>\n","protected":false},"excerpt":{"rendered":"<p>CONTENTS Special Issue:\u201cRecent Progress in Genetic Algorithms \u201d Editors\u2019 Introduction to \u201cRecent Progress in Genetic Algorithms \u201d \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Masayuki Yamamura, Hajime Kita and Isao Ono 477 Advances in Real-Coded Genetic Algorithm Considering Probablistic Distribution Estimation \u2026\u2026\u2026\u2026\u2026.. Jun Sakuma and Shigenobu Kobayashi 479 Perspective on Estimation of Distribution Algorithms \u2014 Bayesian Optimization Algorithm and Its Hybridization \u2014 \u2026\u2026\u2026\u2026\u2026\u2026\u2026 Setsuya Kurahashi, Yuji Katsumata and Takao Terano 487 Multiobjective Design Optimization of Aircraft Configuration \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Shigeru Obayashi 495 Application of Real-Coded Genetic Algorithm to Biological Research Fields \u2014 Inference of Genetic Interactions in Large Scale Gene Networks \u2014 \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 Masahiro Okamoto and Isao Ono 502 Optimization of Uncertain Fitness Functions by Genetic Algorithms [&hellip;]<\/p>\n","protected":false},"author":17,"featured_media":0,"parent":1007042,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":[],"categories":[11],"_links":{"self":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000335"}],"collection":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/comments?post=1000335"}],"version-history":[{"count":3,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000335\/revisions"}],"predecessor-version":[{"id":1008374,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000335\/revisions\/1008374"}],"up":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1007042"}],"wp:attachment":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/media?parent=1000335"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/categories?post=1000335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}