{"id":1213,"date":"2012-11-20T00:09:38","date_gmt":"2012-11-19T15:09:38","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=1213"},"modified":"2012-11-20T00:09:38","modified_gmt":"2012-11-19T15:09:38","slug":"1213","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=1213","title":{"rendered":"SCIS-ISIS 2012"},"content":{"rendered":"

\u795e\u6238\u3067\u958b\u50ac\u3055\u308c\u305f SCIS-ISIS 2012 (The 6th International Conference on Soft Computing and Intelligent Systems The 13th International Symposium on Advanced Intelligent Systems\uff0c http:\/\/scis2012.j-soft.org\/?file=home<\/a> ) \u306b\uff0c\u5ee3\u5b89\u77e5\u4e4b\u6559\u6388\uff0c\u7530\u4e2d\u7f8e\u91cc(D2)\uff0c\u5927\u5800\u88d5\u4e00(M1)\uff0c\u5e03\u5ddd\u5c06\u6765\u4eba(M1)\uff0c\u65e5\u548c\u609f(D1)\u304c\u53c2\u52a0\u3057\u3066\u304d\u307e\u3057\u305f\uff0e\u767a\u8868\u984c\u76ee\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff1a
\n\u25a0Discussion of the crossover method of interactive Genetic Algorithm for extracting multiple peaks on Kansei landscape
\nMisato Tanaka, Tomoyuki Hiroyasu, Mitsunori Miki, Masato Yoshimi, Yasunari Sasaki, Hisatake Yokouchi
\n\u25a0Classification method into determinable and indeterminable areas using SVM and learning data selection
\nTomoyuki Hiroyasu, Yuichi Obori, Hisatake Yokouchi
\n\u25a0Algorithms for Automatic Extraction of Feature Values of Corneal Endothelial Cells using Genetic Programming
\nTomoyuki Hiroyasu, Sakito Nunokawa, Hiroaki Yamaguchi, noriko Koizumi, Naoki Okumura, Hisatake Yokouchi
\n\u25a0Reference Point-Based Search Scheme for Multiobjective Evolutionary Algorithm
\nSatoru Hiwa, Tomoyuki Hiroyasu, Hisatake Yokouchi, Mitsunori Miki, Masashi Nishioka
\nSCIS-ISIS2012\u3067\u306f\uff0c\u30bd\u30d5\u30c8\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\uff0c\u77e5\u7684\u306a\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u57fa\u790e\u7406\u8ad6\u304b\u3089\u5fdc\u7528\u4e8b\u4f8b\u307e\u3067\uff0c\u5e45\u5e83\u3044\u5206\u91ce\u306b\u308f\u305f\u308b\u8b1b\u6f14\u304c\u884c\u308f\u308c\u307e\u3057\u305f\uff0e
\n\u79c1 \u65e5\u548c\u306f\uff0c\u591a\u76ee\u7684\u9032\u5316\u7684\u6700\u9069\u5316\u306b\u95a2\u3059\u308b\u30bb\u30c3\u30b7\u30e7\u30f3\u300cEvolutionary Multiobjective Optimization and Multiple Criteria Decision Making\u300d\u3067\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u306f\uff0c\u56fd\u5185\u306e\u591a\u76ee\u7684\u6700\u9069\u5316\u306e\u8457\u540d\u306a\u7814\u4fee\u8005\u304c\u4f1a\u3057\uff0c\u3053\u306e\u5b66\u4f1a\u306e\u4e2d\u3067\u3082\u304b\u306a\u308a\u306e\u76db\u308a\u4e0a\u304c\u308a\u3092\u898b\u305b\u305f\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u3042\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e
\n\u500b\u4eba\u7684\u306b\u306f\uff0c6\u5e74\u3076\u308a\u306e\u30a2\u30ab\u30c7\u30df\u30c3\u30af\u306a\u5834\u3067\u306e\u767a\u8868\u3068\u306a\u308a\uff0c\u30ac\u30c1\u30ac\u30c1\u306b\u7dca\u5f35\u3057\u3066\u3044\u305f\u306e\u3067\u3059\u304c\uff0c\u5ee3\u5b89\u5148\u751f\u306e\u30b5\u30dd\u30fc\u30c8\u3082\u9802\u304d\u3064\u3064\uff0c\u7121\u4e8b\u767a\u8868\u3092\u7d42\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u4ed6\u5927\u5b66\u306e\u5148\u751f\u65b9\u306b\u3082\u8cb4\u91cd\u306a\u3054\u610f\u898b\u30fb\u3054\u6307\u5c0e\u3092\u9802\u304f\u3053\u3068\u304c\u3067\u304d\uff0c\u5927\u5909\u6709\u610f\u7fa9\u3067\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u306e\u65b9\u5411\u6027\u3084\u8ab2\u984c\u3092\u660e\u78ba\u306b\u3059\u308b\u305f\u3081\u306b\uff0c\u5b66\u4f1a\u767a\u8868\u304c\u6709\u52b9\u306a\u5834\u3067\u3042\u308b\u3053\u3068\u3092\u6539\u3081\u3066\u8a8d\u8b58\u3057\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u3082\u53ef\u80fd\u306a\u9650\u308a\uff0c\u53c2\u52a0\u3057\u3066\u3044\u304d\u305f\u3044\u3082\u306e\u3067\u3059\uff0e\u6b21\u306f\u3082\u3046\u30d6\u30e9\u30f3\u30af\u306f\u8a00\u3044\u8a33\u306b\u306a\u308a\u307e\u305b\u3093\u306d\uff0e\u672c\u5b66\u4f1a\u3078\u306e\u53c2\u52a0\u306b\u969b\u3057\u3066\uff0c\u3054\u5354\u529b\u9802\u304d\u307e\u3057\u305f\u5ee3\u5b89\u5148\u751f\u306f\u3058\u3081MISL\u306e\u7686\u69d8\u306b\u539a\u304f\u5fa1\u793c\u7533\u3057\u4e0a\u3052\u307e\u3059\uff0e
\n\u3010\u6587\u8cac: \u65e5\u548c\u3011
\n<\/p>\n

\n

\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/strong><\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/strong><\/td>\n\u7530\u4e2d\u7f8e\u91cc<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/strong><\/td>\n\u611f\u6027\u30e9\u30f3\u30c9\u30b9\u30b1\u30fc\u30d7\u306e\u591a\u5cf0\u6027\u3092\u62bd\u51fa\u3059\u308b\u5bfe\u8a71\u578b\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4ea4\u53c9\u624b\u6cd5\u306e\u691c\u8a0e<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/strong><\/td>\nDiscussion of the crossover method of interactive Genetic Algorithm for extracting multiple peaks on Kansei landscape<\/td>\n<\/tr>\n
\u8457\u8005<\/strong><\/strong><\/td>\nMisato Tanaka, Tomoyuki Hiroyasu, Mitsunori Miki, Masato Yoshimi, Yasunari Sasaki, Hisatake Yokouchi<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/strong><\/strong><\/td>\n\u65e5\u672c\u77e5\u80fd\u60c5\u5831\u30d5\u30a1\u30b8\u30a3\u5b66\u4f1a<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/strong><\/strong><\/td>\nThe 6th International Conference on Soft Computing and Intelligent Systems & The 13th International Symposium on Advanced Intelligent Systems (SCIS-ISIS2012)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/strong><\/strong><\/td>\nKobe Convention Center, Kobe, Hyogo, Japan<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/strong><\/strong><\/td>\n2012\/11\/20-2012\/11\/24<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n \n<\/div>\n

 
\n1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30
\n2012\u5e7411\u670820\u65e5\u304b\u30892012\u5e7411\u670824\u65e5\u306b\u304b\u3051\u3066\uff0c\u5175\u5eab\u770c\u795e\u6238\u5e02\u795e\u6238\u56fd\u969b\u4f1a\u8b70\u5834\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fSCIS-ISIS2012(The 6th International Conference on Soft Computing and Intelligent Systems & The 13th International Symposium on Advanced Intelligent Systems)1)<\/sup> \u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0eSCIS-ISIS2012\u3067\u306f\uff0c\u30bd\u30d5\u30c8\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\uff0c\u77e5\u7684\u306a\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u57fa\u790e\u7406\u8ad6\u304b\u3089\u5fdc\u7528\u4e8b\u4f8b\u307e\u3067\uff0c\u5e45\u5e83\u3044\u5206\u91ce\u306b\u308f\u305f\u308b\u30c6\u30fc\u30de\u3092\u6271\u3046\u5b66\u4f1a\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3059\uff0e\u79c1\u306f21\u65e5(\u6c34) \u306e\u53e3\u982d\u767a\u8868\u306b\u3066\u8b1b\u6f14\u81f4\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u3055\u3093\uff0c\u79c1\u7530\u4e2d\uff0c\u5e03\u5ddd\uff0c\u5927\u5800\u306e5\u540d\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n 
\n2. \u7814\u7a76\u767a\u8868
\n2.1. \u767a\u8868\u6982\u8981
\n\u79c1\u306f21\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cIntelligent Interaction and Visualization\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c20\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u591a\u5cf0\u6027\u306e\u55dc\u597d\u3092\u8003\u616e\u3057\u305f\u5bfe\u8a71\u578b\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4ea4\u53c9\u624b\u6cd5\u306b\u95a2\u3059\u308b\u691c\u8a0e\u306b\u3064\u3044\u3066\u6271\u3063\u305f\u3082\u306e\u3067\u3059\uff0e\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u306f\u300cDiscussion of the crossover method of in- teractive Genetic Algorithm for extracting multiple peaks on Kansei landscape\u300d\u3067\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
iGA(interactive Genetic Algorithms) is the optimization technique reflecting Kansei because replacing human subjective evaluation with GA\u2019s objective function. Applying iGA to the product recommendation is examined in our study. One of the requirements is to estimate a customer\u2019s multiple preferences and reflect it in the displayed products. When the customer selects the products among a kind of category, he or she may like a product as much as others. In our study, this multiple preference is defined as the multimodal preference. When the customer\u2019s preference is analyzed, the recommendation method displays the more favorite products by considering this multimodal preference. Therefore the iGA to generate offspring with estimating and searching multiple peaks was discussed in this paper. Our proposed method estimates the multiple peaks by clustering the parents evaluated highly and generates the fitting offspring by estimating the distribution of parents within a cluster. We performed the two experiments. In the first experiment, we confirmed that the experiment participants had multimodal preference. In the second experiment, the participants operated the two sys- tem implementing the proposed method or the conventional method. The comparison of the results showed that the system implemented by the proposed method searched the participants\u2019 multimodal preferences more diversely than the conventional method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8a55\u4fa1\u304c\u9032\u3080\u3068\u524d\u306e\u500b\u4f53\u3092\u518d\u8a55\u4fa1\u3067\u304d\u306a\u3044\u304c\uff0c\u305d\u306e\u70b9\u306f\u554f\u984c\u306a\u3044\u306e\u304b<\/strong><\/strong>
\n\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f\u304b\u3089\u9802\u3044\u305f\u8cea\u554f\u3067\u3059\uff0e\u5148\u306b\u8a55\u4fa1\u3057\u305f\u500b\u4f53\u3078\u306e\u8a55\u4fa1\u5024\u304c\u3042\u3068\u3042\u3068\u30e9\u30f3\u30c9\u30b9\u30b1\u30fc\u30d7\u3092\u4f5c\u308b\u969b\u306b\u518d\u78ba\u8a8d\u304c\u5fc5\u8981\u3067\u306f\u306a\u3044\u306e\u304b\u3068\u3044\u3046\u4e3b\u65e8\u306e\u8cea\u554f\u3067\u3042\u308b\u3068\u601d\u3044\u307e\u3059\u304c\uff0c\u305d\u3082\u305d\u3082\u4eca\u56de\u306e\u5b9f\u9a13\u306f\uff12\u5024\u8a55\u4fa1\u3092\u884c\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u4f1d\u308f\u3063\u3066\u3044\u306a\u304b\u3063\u305f\u3088\u3046\u3067\u3059\uff0e\u524d\u306e\u500b\u4f53\u306e\u8a55\u4fa1\u306f\u63a2\u7d22\u3057\u305f\u3044\u7a7a\u9593\u3092\u7d5e\u308b\u3068\u3044\u3046\u76ee\u7684\u306b\u5bfe\u3057\u3066\u96e3\u3057\u3044\u3082\u306e\u3067\u3042\u308a\uff0c\u305f\u3060\uff0c\u305d\u308c\u3092\u3046\u307e\u304f\u56de\u7b54\u3067\u304d\u305a\uff0c\u5c11\u3057\u6df7\u4e71\u3055\u305b\u3066\u3057\u307e\u3063\u305f\u3088\u3046\u306b\u601d\u3044\u307e\u3059\uff0e
\n 
\n2.3. \u611f\u60f3
\n\u4eca\u56de\u306e\u767a\u8868\u3067\u306f15\u5206\u306e\u767a\u8868\u6642\u9593\u306b\u5bfe\u3057\u3066\uff0c\u304e\u308a\u304e\u308a\u307e\u3067\u767a\u8868\u5185\u5bb9\u3092\u8a70\u3081\u8fbc\u3093\u3067\u3057\u307e\u3063\u305f\u7d50\u679c\uff0c\u6700\u5f8c\u306e\u65b9\u306b\u7d50\u679c\u3092\u8a71\u3059\u6642\u9593\u304c\u306a\u304f\u306a\u308b\u3068\u3044\u3046\u4e8b\u614b\u306b\u9665\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u5927\u5909\u306a\u5931\u6557\u3067\u3042\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u6b21\u56de\u304b\u3089\uff0c\u7279\u306b\u82f1\u8a9e\u306e\u767a\u8868\u3067\u306f\uff0c\u3080\u3057\u308d\u4f59\u88d5\u3092\u6301\u3066\u308b\u3088\u3046\u306b\u5168\u4f53\u306e\u6d41\u308c\u3092\u69cb\u6210\u3057\u305f\u4e0a\u3067\uff0c\u6642\u9593\u8abf\u6574\u3092\u304b\u3051\u308b\u3088\u3046\u306b\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u6226\u7565\u3092\u5909\u66f4\u3057\u3066\u884c\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059.
\n 
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Towards Developing Robust Multimodal Databases for Emotion Analysis\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Maria Alejandra Quiros Ramirez, Senya Polikovsky, Yoshinori Kameda, Takehisa Onisawa,\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Knowledge and Information ManagementAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Understanding emotions can make the difference between succeeding and failing during communication. Several systems have been developed in the field of Affective Computing in order to understand emotions. Recently these systems focus into multimodal emotion recognition. The basis of each of these systems is emotion databases. Even though a lot of attention has been placed in capturing spontaneous emotion expressions, building an emotion database is a task with several challenges that are commonly neglected, namely: quality of the recordings, ground truth, multiple device recording, data labeling and context. In this paper we present a new spontaneous emotion database, with human-computer and human to human interactions. This database is composed by eight different synchronized signals, in four interaction tasks. Strategies on how to deal with emotion database construction challenges are explained in detail.
\nIndex Terms\u2014emotion recognition; microexpressions; facial expression; gestures; infrared image; spontaneous emotions; multimodal synchronization.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u767a\u8868\u306f21\u65e5(\u6c34)\u306e\u591c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cKnowledge and Information Management\u300d\u306b\u3066\uff0c\u672c\u7814\u7a76\u5ba4\u306e\u5927\u5800\u306e\u524d\u306b\u8b1b\u6f14\u3055\u308c\u307e\u3057\u305f\uff0e\u30d2\u30c8\u304c\u611f\u60c5\u3092\u8868\u73fe\u3059\u308b\u969b\u306e\u4eba\u9593\u306e\u767a\u3059\u308b\u60c5\u5831\u3092\u30bb\u30f3\u30b5\u304b\u3089\u53d6\u5f97\u3057\u3066\uff0c\u611f\u60c5\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u4f5c\u6210\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u5bfe\u8c61\u3068\u306a\u308b\u60c5\u5831\u3068\u3057\u3066\u306f\uff0c\u30e2\u30fc\u30b7\u30e7\u30f3\u30ad\u30e3\u30d7\u30c1\u30e3\u306b\u3088\u308b\u4f4d\u7f6e\u60c5\u5831\uff0c\u5727\u529b\u30bb\u30f3\u30b5\u306b\u3088\u308b\u8db3\u88cf\u306e\u5727\u529b\uff0c\u30de\u30a4\u30af\u306b\u3088\u308b\u97f3\u58f0\u3084\u58f0\u306e\u30c8\u30fc\u30f3\uff0c\u8d64\u5916\u7dda\u30ab\u30e1\u30e9\u306b\u3088\u308b\u4f4d\u7f6e\u60c5\u5831\u3084\u4f53\u6e29\u306e\u5909\u5316\uff0c\u9ad8\u611f\u5ea6\u30ab\u30e1\u30e9\u306b\u3088\u308b\u52d5\u304d\u3084\u8868\u60c5\u306a\u3069\u304c\u3042\u308a\u307e\u3059\uff0e\u975e\u5e38\u306b\u9769\u65b0\u7684\u306a\u767a\u8868\u3067\uff0c\u3068\u304f\u306b\u591a\u6570\u306e\u30e2\u30c0\u30ea\u30c6\u30a3\u3092\u7528\u3044\u3066\u7d71\u5408\u7684\u306b\u884c\u3063\u3066\u3044\u308b\u3068\u3044\u3046\u70b9\u3067\uff0c\u3068\u3066\u3082\u9762\u767d\u3044\u3068\u601d\u3044\u307e\u3057\u305f\u304c\uff0c\u3069\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u683c\u7d0d\u3059\u308b\u304b\u304c\u4e0d\u660e\u77ad\u3067\uff0c\u305d\u3053\u304c\u5c11\u3057\u6b8b\u5ff5\u3067\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aHeterogeneous Particle Swarm Optimization Including Predator-Prey Relationship\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Akira Hara\uff0cKazumasa Shiraga\uff0cTetsuyuki Takahama\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Intelligent Agent Based Evolutionary Computation IAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Particle Swarm Optimization (PSO) is an optimiza- tion method inspired by the flock behavior of birds. In the original PSO, homogeneous particles search solutions. Several extensions where respective particles can have different search strategies have been proposed. In Heterogeneous PSO (HPSO), respective particles select their own search strategies from a strategy pool, which consists of five kinds of strategies. If the personal best value of a particle has not been improved for some iterations, the particle changes its search strategy. The global search can be performed by the heterogeneity of search strategies. In Predator Prey Optimizer (PPO) is the PSO to which the predator-prey relationship has been introduced. A predator particle moves toward the global best solution, and prey particles have to keep away from the predator particle. Escape from local optima can be performed by the interaction of the two kinds of particles. In this paper, we introduce the predator-prey relationship into the search strategy pool of HPSO. We examine the search performance of our proposed methods and the effect of the diversification of search strategies. Our proposed method with the pool of selected strategies, all of which can be affected by predator, showed the best performance.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u672c\u767a\u8868\u3067\u306fPSO\u306e\u6982\u5ff5\u306b\uff0c\u6355\u98df\u8005\u3068\u975e\u6355\u98df\u8005\u306e\u95a2\u4fc2\u6027\u3092\u6301\u3061\u8fbc\u3093\u3060\u3068\u3044\u3046\u3082\u306e\u3067\uff0c\u30e2\u30c7\u30eb\u306e\u767a\u60f3\u3068\u3057\u3066\u9762\u767d\u3044\u767a\u8868\u3060\u306a\u3068\u611f\u3058\u307e\u3057\u305f\uff0eHPSO(Heterogeneous Particle Swarm Optimization)\u306f\uff0c\u52a0\u901f\u5ea6\u306e\u66f4\u65b0\u30eb\u30fc\u30eb\u304c\u7fa4\u306b\u3088\u3063\u3066\u7570\u306a\u308bPSO\u3068\u306a\u308a\u307e\u3059\u304c\uff0c\u305d\u306e\u7570\u306a\u308b\u7fa4\u306b\u6355\u98df\u8005\u3068\u975e\u6355\u98df\u8005(\u6b63\u78ba\u306b\u306f\u6355\u98df\u8005\u3092\u907f\u3051\u3066\uff0c\u6355\u98df\u8005\u306e\u63a2\u7d22\u3057\u306a\u3044\u6240\u3092\u63a2\u7d22\u3057\u306b\u884c\u304f\u7fa4)\u3068\u3044\u3046\u5f79\u5272\u3092\u3064\u3051\u308b\u3053\u3068\u3067\uff0c\u591a\u69d8\u306a\u89e3\u3092\u63a2\u7d22\u3067\u304d\u308b\u4ed5\u7d44\u307f\u3092\u3068\u3063\u3066\u3044\u308b\u305d\u3046\u3067\u3059\uff0e\u7cbe\u5ea6\u3082\u5411\u4e0a\u3057\u3066\u304a\u308a\uff0c\u9762\u767d\u304b\u3063\u305f\u3067\u3059\uff0e
\n 
\n\u53c2\u8003\u6587\u732e
\n1)\u00a0\u00a0\u00a0 SCIS-ISIS2012\uff0chttp:\/\/scis2012.j-soft.org\/<\/p>\n

\n

\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/b><\/b><\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n
\u5831\u544a\u8005\u6c0f\u540d<\/b><\/b><\/td>\n\u5e03\u5ddd\u5c06\u6765\u4eba<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\n\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u7528\u3044\u305f\u89d2\u819c\u5185\u76ae\u7d30\u80de\u306e\u7279\u5fb4\u91cf\u81ea\u52d5\u62bd\u51fa\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\nAlgorithms for Automatic Extraction of Feature Values of Corneal Endothelial Cells using Genetic Programming<\/td>\n<\/tr>\n
\u8457\u8005<\/b><\/b><\/td>\n\u5ee3\u5b89\u77e5\u4e4b\uff0c\u5e03\u5ddd\u5c06\u6765\u4eba\uff0c\u5c71\u53e3\u6d69\u660e\uff0c\u5c0f\u6cc9\u7bc4\u5b50\uff0c\u5965\u6751\u76f4\u6bc5\uff0c\u6a2a\u5185\u4e45\u731b<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/b><\/b><\/td>\n\u65e5\u672c\u77e5\u80fd\u60c5\u5831\u30d5\u30a1\u30b8\u30a3\u5b66\u4f1a<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/b><\/b><\/td>\nThe 6th International Conference on Soft Computing and Intelligent Systems & The 13th International Symposium on Advanced Intelligent Systems (SCIS-ISIS2012)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/b><\/b><\/td>\n\u795e\u6238\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/b><\/b><\/td>\n2012\/11\/20-2012\/11\/24<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n \n<\/div>\n

 
\n1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30
\n\u79c1\u306f2012\u5e7411\u670820\u65e5\u304b\u30892012\u5e7411\u670824\u65e5\u306b\u304b\u3051\u3066\u5175\u5eab\u770c\u795e\u6238\u5e02\u795e\u6238\u56fd\u969b\u4f1a\u8b70\u5834\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fSCIS&ISIS2012(The 6th International Conference on Soft Computing and Intelligent Systems & The 13th International Symposium on Advanced Intelligent Systems)\u00b9\u207e\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0eSCIS-ISIS2012\u3067\u306f\uff0c\u30bd\u30d5\u30c8\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\uff0c\u77e5\u7684\u306a\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u57fa\u790e\u7406\u8ad6\u304b\u3089\u5fdc\u7528\u4e8b\u4f8b\u307e\u3067\uff0c\u5e45\u5e83\u3044\u5206\u91ce\u306b\u308f\u305f\u308b\u30c6\u30fc\u30de\u3092\u6271\u3046\u5b66\u4f1a\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3059\uff0e\u79c1\u306f23\u65e5(\u91d1) \u306e\u53e3\u982d\u767a\u8868\u306b\u3066\u8b1b\u6f14\u81f4\u3057\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u5ee3\u5b89\u5148\u751f\uff0c\u65e5\u548c\u3055\u3093\uff0c\u7530\u4e2d\u3055\u3093\uff0c\u5e03\u5ddd\uff0c\u5927\u5800\u306e5\u540d\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n 
\n2. \u7814\u7a76\u767a\u8868
\n2.1. \u767a\u8868\u6982\u8981
\n\u79c1\u306f23\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cIntelligent Informatics for Biomedical Research\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c20\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u306b\u3066\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e
\n 
\n\u4eca\u56de\u306e\u767a\u8868\u3067\u306f\uff0c\u6728\u69cb\u9020\u306e\u7d44\u307f\u5408\u308f\u305b\u6700\u9069\u5316\u624b\u6cd5\u3067\u3042\u308b\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u7528\u3044\u3066\uff0c\u89d2\u819c\u5185\u76ae\u7d30\u80de\u753b\u50cf\u3092\u5bfe\u8c61\u3068\u3057\u305f\u7d30\u80de\u9818\u57df\u5206\u5272\u3092\u884c\u3046\u753b\u50cf\u51e6\u7406\u30d5\u30a3\u30eb\u30bf\u3092\u81ea\u52d5\u69cb\u7bc9\u3059\u308b\u624b\u6cd5\u306e\u63d0\u6848\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u306f\u300cAlgorithms for Automatic Extraction of Feature Values of Corneal Endothelial Cells using Genetic Programming\u300d\u3067\u3059\uff0e
\n\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
In cornea tissue engineering, a researcher measures cell density and a form, in order to check the status of a cultivated cell. In this paper, these features values of cells are extracted automatically from corneal endothelial cell images.In the proposed method, genetic programing (GP) is used to construct image filters which can detect cell regions from corneal endothelial cells images. After detecting cell regions, feature values of cells such as density, the number of hexagon cells, and cell sizes are derived. To discuss the effectiveness of the proposed algorithm, the algorithm is applied to 16 sheets of corneal endothelial cells images. The cell region detection process was compared with the results of the Watershed filter which is one of the existing region division filters. From the results, it is confirmed that the filters which can extract cell regions from eight sheets of images with low error compared with the Watershed filter were constructed by GP. At the same time, it is also confirmed that the feature values of cells are detected successfully from five sheets of images.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/b>1<\/b>
\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e
\n\u8cea\u554f\u5185\u5bb9\u306f\uff0c\u5b9f\u969b\u306b\u4f7f\u7528\u3057\u3066\u3044\u308bGP\u306e\u30e2\u30c7\u30eb\u306f\u4f55\u304b\uff1f\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e
\n\u79c1\u306fSGP(\u30b7\u30f3\u30d7\u30ebGP)\u3092\u7528\u3044\u3066\u3044\u307e\u3059\uff0e\u3068\u56de\u7b54\u3055\u305b\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e
\n 
\n2.3. \u611f\u60f3
\n\u7df4\u7fd2\u3067\u306f\uff0c\u6642\u9593\u901a\u308a\u306b\u767a\u8868\u3067\u304d\u3066\u3044\u305f\u306e\u3067\u3059\u304c\uff0c\u672c\u756a\u3067\u7dca\u5f35\u3057\u3066\u3057\u307e\u3044\uff0c\u767a\u8868\u304c\u65e9\u304f\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u82f1\u8a9e\u80fd\u529b\u304c\u8db3\u308a\u305a\uff0c\u8cea\u554f\u3092\u3059\u3050\u306b\u7406\u89e3\u3067\u304d\u306a\u304b\u3063\u305f\u70b9\u306f\u53cd\u7701\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3068\u304b\u3093\u3058\u307e\u3057\u305f\uff0e\u7df4\u7fd2\u3092\u7e70\u308a\u8fd4\u3059\u4e8b\u3068\u82f1\u8a9e\u306e\u52c9\u5f37\u306f\u3084\u306f\u308a\u91cd\u8981\u3067\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n 
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000A Fundamental Study on the Effectiveness of Immune Algorithm for Multi-Objective 0\/1 Knapsack Problem\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Satoshi Ono, Ryota Morishige, Shigeru Nakayama
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Evolutionary Multiobjective Optimization and Multiple Criteria Decision Making II
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Immune Algorithms (IAs) are categorized into three
\nclasses. Although the major type of IAs using clonal selection
\nhave been widely investigated and applied to Multi-objective
\nOptimization Problems (MOPs), IAs based on self-regulation by
\nsuppressor T-cells have not. This paper focuses on the latter
\nIAs and proposes Non-dominated Prioritized IA (NPIA) which
\nis designed to solve MOPs with keeping the characteristics of IAs
\nwithout clonal selection. Experimental results have shown NPIA\u2019s
\nsearch performance competitive with MOGAs and possibility of
\nIA-based multi-objective optimization.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u767a\u8868\u306f\u30d2\u30e5\u30fc\u30ea\u30b9\u30c6\u30a3\u30c3\u30af\u306a\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u3072\u3068\u3064\u3067\u3042\u308b\u514d\u75ab\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u591a\u76ee\u7684\u554f\u984c\u306b\u9069\u5fdc\u3055\u305b\u305f\u591a\u76ee\u7684\u514d\u75ab\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e
\n\u672c\u767a\u8868\u3067\u306f\u514d\u75ab\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u591a\u76ee\u7684\u5316\u306b\u975e\u512a\u8d8a\u30bd\u30fc\u30c8\u3092\u7528\u3044\u3066\u304a\u308a\uff0c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8abf\u6574\u3059\u308b\u4e8b\u306b\u3088\u3063\u3066\u4ed6\u306e\u591a\u76ee\u7684\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u5339\u6575\u3059\u308b\u6027\u80fd\u3092\u5f15\u304d\u51fa\u305b\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e
\n\u79c1\u3082\u6700\u9069\u5316\u306e\u30c6\u30fc\u30de\u306b\u304a\u3044\u3066\u65e2\u5b58\u306e\u624b\u6cd5\u306b\u52a3\u3089\u306a\u3044\u624b\u6cd5\u3092\u63d0\u6848\u3067\u304d\u308b\u3088\u3046\u306b\u9811\u5f35\u3063\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aSituation-Oriented Clustering of Sightseeing SpotImages Using Visual and Tag Information
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Chia-Huang Chen, Yasufumi Takama
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Intelligent Interaction and Visualization
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Now a day, the tourists get used to take many photos
\nin a journey and share these sightseeing spot images on album
\nwebsites. The meaningful grouping of these images will become
\nimportant and useful. In particular, sightseeing spot scenes are
\nvary with different situations, such as weather conditions and
\nseasons. Thus the categorization of different situations is expected to be beneficial for tourists to plan when to visit there.
\nThis paper proposes a hybrid approach which integrates contentbased image clustering with filtering based on tag information of
\nimage. Content-based image clustering categorizes sightseeing
\nspot images into night, sunrise\/sunset, cloudy, and shine situations based on color feature extraction from ROI (region of interest). By using geotag information, collected images can be limited to a reasonable boundary to eliminate outliers. Furthermore, by using the timestamp of images, the four situation categories constructed by content-based image clustering are further verified to
\nincrease the accuracy. Experimental results show that the hybrid
\napproach of content-based image clustering and tag-based filtering is effective for obtaining clusters with high precision and recall.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u767a\u8868\u306f\u5927\u91cf\u306e\u98a8\u666f\u5199\u771f\u3092\u64ae\u5f71\u6642\u306b\u5929\u5019\u6bce\u3067\u5206\u985e\u3059\u308b\u624b\u6cd5\u306e\u63d0\u6848\u3067\u3057\u305f\uff0e
\n\u63d0\u6848\u624b\u6cd5\u3067\u306f\uff0c\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u624b\u6cd5\u3068\u5199\u771f\u306e\u30bf\u30b0\u60c5\u5831\u306b\u57fa\u3065\u304f\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3092\u7528\u3044\u308b\u4e8b\u3067\uff0c\u540c\u69d8\u306e\u666f\u8272\u306e\u5199\u771f\u3092\u5929\u5019\u6bce\u306b\u5206\u985e\u3057\u305f\u7d50\u679c\u306b\u3064\u3044\u3066\u767a\u8868\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e
\n\u3053\u306e\u624b\u6cd5\u3067\u306f\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3068\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u30cf\u30a4\u30d6\u30ea\u30c3\u30c8\u65b9\u6cd5\u306b\u91cd\u70b9\u3092\u7f6e\u304b\u308c\u3066\u3044\u307e\u3057\u305f\uff0e
\n\u4eca\u5f8c\u306f\u69d8\u3005\u306a\u5206\u91ce\u306b\u304a\u3044\u3066\u3053\u306e\u3088\u3046\u306a\u30cf\u30a4\u30d6\u30ea\u30c3\u30c8\u624b\u6cd5\u304c\u591a\u304f\u63d0\u6848\u3055\u308c\u3066\u3044\u304f\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u306e\u3067\uff0c\u305d\u3046\u3044\u3063\u305f\u8003\u3048\u65b9\u3082\u53d6\u308a\u5165\u308c\u3066\u3044\u304d\u305f\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n 
\n\u53c2\u8003\u6587\u732e
\n1)\u00a0\u00a0\u00a0 SCIS&ISIS2012, http:\/\/scis2012.j-soft.org\/?file=home
\n <\/p>\n

\n

\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/b><\/b><\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/b>
\n\u5831\u544a\u8005\u6c0f\u540d<\/b><\/b><\/td>\n		 
\n\u65e5\u548c\u3000\u609f<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\nConference Report<\/td>\n<\/tr>\n
\u8457\u8005<\/b><\/b><\/td>\n\u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b, \u6a2a\u5185\u4e45\u731b\uff0c\u4e09\u6728\u5149\u7bc4\uff0c\u897f\u5ca1\u96c5\u53f2<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/b><\/b><\/td>\n\u533b\u7642\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u5ba4<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/b><\/b><\/td>\nSCIS-ISIS 2012 (The 6th International Conference on Soft Computing and Intelligent Systems The 13th International Symposium on Advanced Intelligent Systems)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/b><\/b><\/td>\n\u795e\u6238\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\uff08\u5175\u5eab\u770c\u795e\u6238\u5e02\uff09<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/b><\/b><\/td>\n2012\/11\/20-2012\/11\/24<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n \n<\/div>\n

 
\n1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30
\n2012\/11\/20\u304b\u30892012\/11\/24\u306b\u304b\u3051\u3066\uff0c\u795e\u6238\u3067\u958b\u50ac\u3055\u308c\u305f SCIS-ISIS 2012 (The 6th International Conference on Soft Computing and Intelligent Systems The 13th International Symposium on Advanced Intelligent Systems\uff0c http:\/\/scis2012.j-soft.org\/?file=home ) \u306b\u53c2\u52a0\u3057\u3066\u304d\u307e\u3057\u305f\uff0eSCIS-ISIS2012\u3067\u306f\uff0c\u30bd\u30d5\u30c8\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\uff0c\u77e5\u7684\u306a\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u57fa\u790e\u7406\u8ad6\u304b\u3089\u5fdc\u7528\u4e8b\u4f8b\u307e\u3067\uff0c\u5e45\u5e83\u3044\u5206\u91ce\u306b\u308f\u305f\u308b\u8b1b\u6f14\u304c\u884c\u308f\u308c\u307e\u3057\u305f\uff0e
\n 
\n2. \u7814\u7a76\u767a\u8868
\n2.1. \u767a\u8868\u6982\u8981
\n\u79c1\u306f\uff0c23\u65e5\u306b\u958b\u50ac\u3055\u308c\u305f\u591a\u76ee\u7684\u9032\u5316\u7684\u6700\u9069\u5316\u306b\u95a2\u3059\u308b\u30bb\u30c3\u30b7\u30e7\u30f3\u300cEvolutionary Multiobjective Optimization and Multiple Criteria Decision Making II\u300d\u3067\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u767a\u8868\u6642\u9593\u306f\u8cea\u7591\u3092\u542b\u308125\u5206\u3067\u3057\u305f\uff0e\u3053\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u306f\uff0c\u56fd\u5185\u306e\u591a\u76ee\u7684\u6700\u9069\u5316\u306e\u8457\u540d\u306a\u7814\u4fee\u8005\u304c\u4f1a\u3057\uff0c\u3053\u306e\u5b66\u4f1a\u306e\u4e2d\u3067\u3082\u304b\u306a\u308a\u306e\u76db\u308a\u4e0a\u304c\u308a\u3092\u898b\u305b\u305f\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u3042\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e
\n\u79c1\u306e\u767a\u8868\u306f\uff0c\u591a\u76ee\u7684\u6700\u9069\u5316\u306e\u305f\u3081\u306e\u65b0\u3057\u3044\u63a2\u7d22\u30b9\u30ad\u30fc\u30e0\u306e\u63d0\u6848\u3067\u3059\uff0e\u591a\u76ee\u7684\u6700\u9069\u5316\u3067\u975e\u52a3\u89e3\u96c6\u5408\u306b\u6c42\u3081\u3089\u308c\u308b\u6027\u8cea\u3068\u3057\u3066\uff0c\u30d1\u30ec\u30fc\u30c8\u6700\u9069\u89e3\u306b\u53ce\u675f\u3057\u3066\u3044\u308b\u3053\u3068\uff08\u7cbe\u5ea6\uff09\uff0c\u5e45\u5e83\u3055\uff0c\u5747\u4e00\u6027\u306a\u3069\u304c\u3042\u308a\u307e\u3059\uff0e\u63d0\u6848\u3057\u305f\u63a2\u7d22\u30b9\u30ad\u30fc\u30e0\u3067\u306f\uff0c\u53c2\u7167\u70b9\u3092\u7528\u3044\u305f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u5e45\u5e83\u3055\u306e\u5411\u4e0a\u3092\u91cd\u8996\u3057\u305f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\uff0c\u7cbe\u5ea6\u3068\u5e45\u5e83\u3055\u306e\u5411\u4e0a\u3092\u4e21\u7acb\u3055\u305b\u3088\u3046\u3068\u3059\u308b\u3082\u306e\u3067\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
In multiobjective optimization problems, it is important to derive solutions with high accuracy, uniform distribution, and broadness. Here, we propose a two-phase search process to improve the accuracy and broadness of solutions. In the first phase, the accuracy of the solutions is improved, and a reference point specified by a decision maker is set and utilized for the search. In the second phase, the solutions are broadened using the Distributed Cooperation Scheme. The numerical results indicated that the proposed search scheme was capable of deriving broader solutions than the conventional multiobjective evolutionary algorithm without deterioration of accuracy.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/b><\/b>
\nQ. \u6bd4\u8f03\u624b\u6cd5\u306b\u4f7f\u3063\u3066\u3044\u308bNSGA-II\u306f\u901a\u5e38\uff08\u30aa\u30ea\u30b8\u30ca\u30eb\uff09\u306e\u3082\u306e\u304b\uff0e\uff08\u5927\u962a\u5e9c\u7acb\u5927\u5b66\uff0c\u77f3\u6e15\u5148\u751f\uff09
\nA. \u30aa\u30ea\u30b8\u30ca\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\uff0e
\n\u2192\u3053\u308c\u306b\u5bfe\u3057\u3066\uff0c\u3088\u308a\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u308b\uff08\u63a2\u7d22\u6bcd\u96c6\u56e3\u3092\u5e83\u3052\u308b\uff09\u305f\u3081\u306b\u306f\uff0c\u5358\u76ee\u7684\u6700\u9069\u5316\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6539\u5584\u3059\u308b\u3088\u308a\u3082\uff0c\u3069\u306e\u3088\u3046\u306a\u500b\u4f53\u3068\u4ea4\u53c9\u3057\u3066\u3044\u308b\u304b\u3092\u8a73\u7d30\u306b\u5206\u6790\u3057\u3066\uff0c\u89e3\u96c6\u5408\u304c\u5e83\u304c\u308b\u3088\u3046\u306a\u4ea4\u53c9\u5bfe\u8c61\u3092\u9078\u3076\u3088\u3046\u306b\u3057\u305f\u65b9\u304c\u826f\u3044\u3068\u306e\u30b3\u30e1\u30f3\u30c8\u3092\u9802\u304d\u307e\u3057\u305f\uff0e
\n 
\n2.3. \u611f\u60f3
\n6\u5e74\u3076\u308a\u306e\u30a2\u30ab\u30c7\u30df\u30c3\u30af\u306a\u5834\u3067\u306e\u767a\u8868\u3068\u306a\u308a\uff0c\u5927\u5909\u7dca\u5f35\u3057\u3066\u3044\u305f\u306e\u3067\u3059\u304c\uff0c\u5ee3\u5b89\u5148\u751f\u306e\u30b5\u30dd\u30fc\u30c8\u3082\u9802\u304d\u3064\u3064\uff0c\u7121\u4e8b\u767a\u8868\u3092\u7d42\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u4ed6\u5927\u5b66\u306e\u5148\u751f\u65b9\u306b\u3082\u8cb4\u91cd\u306a\u3054\u610f\u898b\u30fb\u3054\u6307\u5c0e\u3092\u9802\u304f\u3053\u3068\u304c\u3067\u304d\uff0c\u5927\u5909\u6709\u610f\u7fa9\u3067\u3057\u305f\uff0e\u81ea\u5206\u306e\u7814\u7a76\u306e\u65b9\u5411\u6027\u3084\u8ab2\u984c\u3092\u660e\u78ba\u306b\u3059\u308b\u305f\u3081\u306b\uff0c\u5b66\u4f1a\u767a\u8868\u304c\u6709\u52b9\u306a\u5834\u3067\u3042\u308b\u3053\u3068\u3092\u6539\u3081\u3066\u8a8d\u8b58\u3057\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u3082\u53ef\u80fd\u306a\u9650\u308a\uff0c\u53c2\u52a0\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u672c\u5b66\u4f1a\u3078\u306e\u53c2\u52a0\u306b\u969b\u3057\u3066\uff0c\u3054\u5354\u529b\u9802\u304d\u307e\u3057\u305f\u5ee3\u5b89\u5148\u751f\u306f\u3058\u3081MISL\u306e\u7686\u69d8\u306b\u539a\u304f\u5fa1\u793c\u7533\u3057\u4e0a\u3052\u307e\u3059\uff0e
\n 
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u8074\u8b1b\u3057\u305f\u767a\u8868\u306e\u4e2d\u3067\uff0c\u5370\u8c61\u306b\u6b8b\u3063\u305f\u3082\u306e\u306e\u6982\u8981\u3092\uff12\u3064\u8a18\u8f09\u3057\u307e\u3059\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000A study on Two-Step Search using Global-Best in PSO for Multi-objective Optimization Problems
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hiroyuki Hirano, Tomohiro Yoshikawa
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Evolutionary Multiobjective Optimization and Multiple Criteria Decision Making III<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u3053\u306e\u767a\u8868\u306f\u5358\u76ee\u7684\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306ePSO\u3092\u7528\u3044\u3066\u591a\u76ee\u7684\u6700\u9069\u5316\u306b\u30a2\u30d7\u30ed\u30fc\u30c1\u3057\u3066\u304a\u308a\uff0c\u79c1\u304c\u63d0\u6848\u3057\u3066\u3044\u308b\u63a2\u7d22\u30b9\u30ad\u30fc\u30e0\u3068\u975e\u5e38\u306b\u985e\u4f3c\u3057\u305f\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u767a\u8868\u3067\u306e\u30dd\u30a4\u30f3\u30c8\u306f2\u6bb5\u968e\u76ee\u3067PSO\u3092\u9069\u7528\u3059\u308b\u969b\uff0c\u8907\u6570\u3042\u308b\u76ee\u7684\u95a2\u6570\u306b\u5bfe\u3057\u3066\u8907\u6570\u306ePSO\u500b\u4f53\u7fa4\u3092\u8a2d\u3051\u3066\uff0c\u5404\u500b\u4f53\u7fa4\u306b\u5225\u3005\u306e\u5358\u4e00\u76ee\u7684\u3092\u8a2d\u5b9a\u3057\uff0c\u6b8b\u308a\u306e\u76ee\u7684\u95a2\u6570\u3092\u5236\u7d04\u6761\u4ef6\u3068\u3057\u3066\u89e3\u3044\u3066\u3044\u308b\u70b9\u3067\u3059\uff0e\u3053\u306e\u5834\u5408\uff0c\u5358\u76ee\u7684\u500b\u4f53\u7fa4\u3067\u3042\u3063\u3066\u3082\u8907\u6570\u306e\u76ee\u7684\u95a2\u6570\u3092\u8003\u616e\u3067\u304d\u307e\u3059\uff0e\u3053\u306e\u8003\u3048\u65b9\u306f\u79c1\u306e\u63d0\u6848\u3057\u3066\u3044\u308b\u63a2\u7d22\u30b9\u30ad\u30fc\u30e0\u306b\u3082\u53d6\u308a\u5165\u308c\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3068\u601d\u3044\u307e\u3059\uff0e\u305f\u3060\u3057\uff0c\u5f97\u3089\u308c\u308b\u89e3\u306e\u5747\u4e00\u6027\u306e\u9762\u3067\u306f\u89e3\u306e\u201c\u629c\u3051\u201d\u304c\u591a\u3044\u3088\u3046\u306b\u3082\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u308c\u306f\u79c1\u306e\u63d0\u6848\u624b\u6cd5\u306b\u7f6e\u3044\u3066\u3082\u540c\u69d8\u3067\uff0c\u88ab\u8986\u7387\u3092\u4e0a\u3052\u308b\u3088\u3046\u306a\u4ed5\u7d44\u307f\u304c\u5fc5\u8981\u3068\u601d\u308f\u308c\u307e\u3059\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aPerformance Comparison of Evolutionary Algorithms Applied to Hybrid Rocket Problem
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Kazuhisa Chiba
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Evolutionary Multiobjective Optimization and Multiple Criteria Decision Making II<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u30d1\u30ec\u30fc\u30c8\u89e3\u304b\u3089\u306e\u60c5\u5831\u62bd\u51fa\u3092\u884c\u3046\u5834\u5408\uff0c\u3088\u308a\u591a\u304f\u306e\u60c5\u5831\u3092\u5f97\u308b\u305f\u3081\u306b\u306f\uff0c\u826f\u597d\u306a\u30d1\u30ec\u30fc\u30c8\u89e3\u3092\u5f97\u3089\u308c\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u9078\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\uff0e\u3053\u306e\u767a\u8868\u3067\u306f\uff0cGA\uff0cDE\uff0cPSO\u306a\u3069\u8907\u6570\u306e\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u300c\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30ed\u30b1\u30c3\u30c8\u306e\u6982\u5ff5\u8a2d\u8a08\u554f\u984c\u300d\u306b\u9069\u7528\u3057\uff0c\u4ea4\u53c9\u6cd5\u306e\u9055\u3044\u306b\u3088\u308b\u5f71\u97ff\u307e\u3067\u542b\u3081\u3066\u8907\u6570\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6027\u80fd\u3092\u6bd4\u8f03\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u7d50\u679c\u306fGA\u3068DE\u306e\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u624b\u6cd5\u3067\uff0cPrincipal component analysis blended crossover (PCABLX) \u307e\u305f\u306fConfidence interval based crossover\uff08CIX\uff09\u3092\u4ea4\u53c9\u6cd5\u3068\u3057\u3066\u7528\u3044\u308b\u5834\u5408\u304c\u826f\u597d\u3067\u3042\u3063\u305f\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u4e00\u65b9\u3067\u79c1\u306f\u3053\u306e\u7d50\u679c\u3088\u308a\u3082\uff0c\u5343\u8449\u5148\u751f\u304c\u30d1\u30ec\u30fc\u30c8\u89e3\u306e\u8a2d\u8a08\u60c5\u5831\u3092\u62bd\u51fa\u3059\u308b\u624b\u6bb5\u3068\u3057\u3066\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u3092\u7528\u3044\u3089\u308c\u3066\u304a\u308a\uff0c\u305d\u306e\u7d50\u679c\u304c\u30d1\u30ec\u30fc\u30c8\u89e3\u306e\u7279\u5fb4\u3092\u5b9a\u6027\u7684\u306b\u8868\u3059\u306e\u306b\u975e\u5e38\u306b\u308f\u304b\u308a\u3084\u3059\u3044\u56f3\u306b\u306a\u3063\u3066\u3044\u305f\u70b9\u306b\u611f\u9298\u3092\u53d7\u3051\u307e\u3057\u305f\uff0eSOM\u306e\u6b20\u70b9\u3068\u3057\u3066\uff0cSOM\u4e0a\u306e\u5024\u305d\u306e\u3082\u306e\u304c\u610f\u5473\u306e\u3042\u308b\u5024\u306b\u306a\u3063\u3066\u3044\u306a\u3044\u305f\u3081\uff0c\u5b9a\u91cf\u7684\u306a\u5206\u6790\u304c\u3057\u3065\u3089\u3044\u3053\u3068\u304c\u3042\u308b\u3068\u500b\u4eba\u7684\u306b\u306f\u611f\u3058\u3066\u3044\u305f\u306e\u3067\u3059\u304c\uff0c\u591a\u6b21\u5143\u306e\u30c7\u30fc\u30bf\u306e\u7279\u5fb4\u3092\u8996\u899a\u5316\u3057\uff0c\u5b9a\u6027\u7684\u306b\u7279\u5fb4\u3092\u3064\u304b\u3080\u610f\u5473\u3067\u306f\uff0cSOM\u3082\u6709\u52b9\u3067\u3042\u308b\u3068\u601d\u3044\u76f4\u3059\u6a5f\u4f1a\u3068\u306a\u308a\uff0c\u6709\u610f\u7fa9\u3067\u3057\u305f\uff0e
\n <\/p>\n

\n

\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/b><\/b><\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/b>
\n\u5831\u544a\u8005\u6c0f\u540d<\/b><\/b><\/td>\n		 
\n\u7530\u4e2d\u7f8e\u91cc<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\n\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0fMRI\u306b\u3088\u308b\u5bfe\u8a71\u578b\u6700\u9069\u5316\u30b7\u30b9\u30c6\u30e0\u306e\u691c\u8a0e<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/b><\/b><\/td>\nDiscussion of interactive optimization system in Real-time fMRI<\/td>\n<\/tr>\n
\u8457\u8005<\/b><\/b><\/td>\n\u7530\u4e2d\u7f8e\u91cc\uff0c\u5c71\u672c\u8a69\u5b50\uff0c\u5ee3\u5b89\u77e5\u4e4b\uff0c\u4e09\u6728\u5149\u7bc4<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/b><\/b><\/td>\n\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/b><\/b><\/td>\n2013\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a (\u7b2c27\u56de)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/b><\/b><\/td>\n\u5bcc\u5c71\u770c\u5bcc\u5c71\u5e02\u5bcc\u5c71\u56fd\u969b\u4f1a\u8b70\u5834 \u307b\u304b<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/b><\/b><\/td>\n2013\/6\/4-2013\/6\/7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n \n<\/div>\n

 
\n1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30
\n2013\/6\/4\u304b\u30892013\/6\/7\u306b\u304b\u3051\u3066\uff0c\u5bcc\u5c71\u770c\u5bcc\u5c71\u5e02\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c27\u56de\u5168\u56fd\u5927\u4f1a\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u79c1\u4ee5\u5916\u306bM2\u306e\u798f\u5cf6\u304c\u53c2\u52a0\u3057\uff0c\u6700\u7d42\u65e5\u306b\u306f\u5ee3\u5b89\u5148\u751f\u3082\u8a55\u8005\u3068\u3057\u3066\u53c2\u52a0\u3055\u308c\u307e\u3057\u305f\uff0e\u672c\u5927\u4f1a\u306fAI\u6280\u8853\u306b\u95a2\u9023\u3059\u308b\u591a\u304f\u306e\u30c6\u30fc\u30de\u3092\u6271\u3063\u3066\u304a\u308a\uff0c\u7406\u8ad6\u5206\u91ce\u304b\u3089\u6a5f\u68b0\u5b66\u7fd2\uff0c\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff0c\u753b\u50cf\u51e6\u7406\uff0c\u307e\u305f\u8fd1\u5e74\u3067\u306fWeb\u30c7\u30fc\u30bf\u306a\u3069\u306b\u4ee3\u8868\u3055\u308c\u308b\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u3084\uff0c\u305d\u308c\u3089\u306e\u30c7\u30fc\u30bf\u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u306b\u95a2\u3059\u308b\u7814\u7a76\u306a\u3069\u591a\u69d8\u306a\u30c6\u30fc\u30de\u3092\u6271\u3046\u975e\u5e38\u306b\u5e45\u5e83\u3044\u5b66\u4f1a\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3059\uff0e\u305d\u306e\u305f\u3081\u767a\u8868\u4ef6\u6570\u3082800\u4ef6\u3068\u975e\u5e38\u306b\u591a\u304f\uff0c\u53c2\u52a0\u8005\u6570\u306b\u81f3\u3063\u3066\u306f\uff0c\u306e\u30791000\u4eba\u3092\u8d85\u3048\u308b\u5927\u898f\u6a21\u306a\u5927\u4f1a\u3068\u306a\u3063\u3066\u304a\u308a\uff0c\u591a\u6570\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u304c\u30d1\u30e9\u30ec\u30eb\u306b\u958b\u50ac\u3055\u308c\uff0c\u898b\u3066\u56de\u308b\u306e\u304c\u5927\u5909\u3067\u3057\u305f\uff0e\u4e00\u90e8\u306e\u4f1a\u5834\u306f\u5730\u5143\u5e02\u6c11\u3078\u3082\u516c\u958b\u3055\u308c\uff0c\u56f2\u7881\u30d7\u30ed\u30b0\u30e9\u30e0\u3068\u68cb\u58eb\u3068\u306e\u5bfe\u6226\u306a\u3069\u304c\u6ce8\u76ee\u3092\u96c6\u3081\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u5927\u4f1a\u53c2\u52a0\u8005\u4ee5\u5916\u306e\u61c7\u89aa\u4f1a\u3082\u5546\u5e97\u8857\u306e\u4e00\u533a\u753b\u3092\u8cb8\u3057\u5207\u3063\u3066\u884c\u308f\u308c\u308b\u306a\u3069\uff0c\u975e\u5e38\u306b\u6d3b\u6c17\u306e\u3042\u308b\u5927\u4f1a\u3067\u3057\u305f\uff0e
\n 
\n2. \u7814\u7a76\u767a\u8868
\n2.1. \u767a\u8868\u6982\u8981
\n\u79c1\u306f6\u65e5\u306e\u5348\u5f8c\u306e\u30aa\u30fc\u30ac\u30ca\u30a4\u30ba\u30c9\u30bb\u30c3\u30b7\u30e7\u30f3\u300c\u8133\u79d1\u5b66\u3068AI\u300d\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0e15\u5206\u306e\u8b1b\u6f14\u3068\uff0c5\u5206\u306e\u8cea\u7591\u304c\u5272\u308a\u5f53\u3066\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u767a\u8868\u306f\u5bfe\u8a71\u578b\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u751f\u4f53\u60c5\u5831\u3092\u4f7f\u3046\u3053\u3068\u3092\u6700\u7d42\u76ee\u6a19\u3068\u3057\u3066\uff0c\u305d\u306e\u4e88\u5099\u691c\u8a0e\u306e\u5185\u5bb9\u3067\u306e\u767a\u8868\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3059\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
In this research it is discussed that how to employ human biological signals, especially functional information of brain, as evaluation values of candidate solutions and what kind of system should be developed in interactive genetic algorithms. To perform this goal, in this paper, an experiment was performed in order to examine rela- tionship between experimental participants\u2019 brain activation measured by fMRI (functional Magnetic Resonance Imaging) and evaluation values based on their preferences when picture images of foods were shown. The brain activation patterns were extracted from brain regions where we observed highly significant activations at the time of presentation. Then, they were classified into preference or unpreference patterns. The accuracy rates were higher than a chance level in all the participants.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u898b\u304b\u3051\u304c\u30c0\u30a4\u30ec\u30af\u30c8\u306b\u55dc\u597d\u306b\u5f71\u97ff\u3059\u308b\u3082\u306e\u3092\u9078\u629e\u3059\u308b\u3079\u304d\u3067\u306f\uff1f\uff1f<\/b><\/b>
\n\u548c\u6b4c\u5c71\u5927\u5b66\u306e\u66fd\u6211\u5148\u751f\u3088\u308a\u9802\u3044\u305f\u8cea\u554f\u3067\u3059\uff0e\u3053\u308c\u306b\u3064\u3044\u3066\u306f\u30c7\u30b6\u30a4\u30f3\u3067\u65e2\u306b\u5b9f\u9a13\u6e08\u307f\u3067\u3042\u308b\u3053\u3068\uff0c\u3042\u307e\u308a\u826f\u597d\u306a\u7d50\u679c\u304c\u5f97\u3089\u308c\u306a\u304b\u3063\u305f\u3053\u3068\u3092\u3054\u8aac\u660e\u3057\uff0c\u5148\u884c\u7814\u7a76\u306b\u591a\u3044\u98df\u54c1\u3092\u9078\u629e\u3057\u305f\u3053\u3068\u3092\u8ff0\u3079\u307e\u3057\u305f\uff0e\u7d9a\u3051\u3066\uff0c\u98df\u54c1\u3092\u4e8b\u524d\u306b\u5473\u898b\u3055\u305b\u305f\u65b9\u304c\u826f\u3044\u306e\u3067\u306f\uff0c\u3068\u3044\u3046\u3054\u6307\u6458\u3082\u9802\u304d\u307e\u3057\u305f\uff0e\u3053\u308c\u306b\u3064\u3044\u3066\u306f\u4eca\u5f8c\u691c\u8a0e\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u8003\u3048\u3066\u304a\u308a\u307e\u3059\uff0e
\n 
\n\u30fb\u8a55\u4fa1\u3060\u3051\u3067\u306a\u304f\uff0c\u8133\u6d3b\u52d5\u306f\u904e\u53bb\u306e\u8a55\u4fa1\u306b\u60d1\u308f\u3055\u308c\u306a\u3044\u306e\u304b\uff1f<\/b><\/b>
\n\u30fb\u3046\u305d\u767a\u898b\u5668\u7684\u306a\u3082\u306e\u3067\u306f\u3088\u3044\u3068\u601d\u3046\u304c\uff0c\u3046\u305d\u3092\u3064\u304f\u5fc5\u8981\u306f\u3053\u306e\u5b9f\u9a13\u306b\u306f\u306a\u3044\u306e\u3067\uff0c\u305d\u3053\u307e\u3067<\/b>fMRI<\/b>\u304c\u52b9\u679c\u7684\u306a\u306e\u304b\uff1f<\/b><\/b>
\n\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f\u304b\u3089\u9802\u3044\u305f\u8cea\u554f\u3067\u3059\uff0e\u524d\u8005\u306b\u3064\u3044\u3066\u306f\uff0c\u5c11\u306a\u304f\u3068\u3082\u8a55\u4fa1\u3092\u624b\u4f5c\u696d\u3067\u884c\u3046\u5834\u5408\u306f\uff0c\u77ed\u671f\u8a18\u61b6\u306b\u6b8b\u3063\u3066\u3044\u308b\u985e\u4f3c\u3057\u305f\u5225\u306e\u5019\u88dc\u3078\u306e\u8a55\u4fa1\u5024\u304c\u5f71\u97ff\u3057\u3066\u304f\u308b\u3053\u3068\u3092\u8aac\u660e\u3057\u305f\u4e0a\u3067\uff0c\u5c11\u306a\u304f\u3068\u3082\u305d\u306e\u30ce\u30a4\u30ba\u3092\u6d88\u53bb\u3067\u304d\u308b\u65e8\u3092\u3054\u8aac\u660e\u3057\u307e\u3057\u305f\uff0e
\n\u307e\u305f\uff0c\u5f8c\u8005\u306b\u3064\u3044\u3066\u306f\uff0c\u5618\u3092\u4ed8\u304f\uff0c\u4ed8\u304b\u306a\u3044\u306e\u3088\u3046\u306a\u30b7\u30c1\u30e5\u30a8\u30fc\u30b7\u30e7\u30f3\u3067\u306a\u304f\uff0c\u88ab\u9a13\u8005\u81ea\u8eab\u304c\u306f\u3063\u304d\u308a\u3068\u8868\u73fe\u3067\u304d\u306a\u3044\u597d\u307f\u306e\u8a55\u4fa1\u3092\u8aad\u307f\u53d6\u308b\u3053\u3068\u304c\u76ee\u7684\u3067\u3042\u308b\u3068\u8aac\u660e\u81f4\u3057\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8996\u7dda\u3092\u8a08\u6e2c\u3057\u3066\u95a2\u5fc3\u5ea6\u3092\u56f3\u308b\u306e\u306f\uff1f<\/b><\/b>
\n\u7523\u696d\u7dcf\u5408\u7814\u7a76\u6240\u306e\u4e00\u6749\u5148\u751f\u304b\u3089\u9802\u3044\u305f\u8cea\u554f\u3067\u3059\uff0e\u4e0a\u624b\u304f\u7b54\u3048\u3089\u308c\u306a\u3044\u8cea\u554f\u3067\u3057\u305f\uff0e\u7814\u7a76\u5ba4\u3067\u8996\u7dda\u306b\u3064\u3044\u3066\u3082\u53d6\u308a\u7d44\u3093\u3067\u304a\u308a\uff0c\u76f8\u4e92\u88dc\u5b8c\u7684\u306b\u4f7f\u3048\u308c\u3070\u30d9\u30b9\u30c8\u3067\u3042\u308b\uff0c\u3068\u3044\u3046\u3088\u3046\u306a\u56de\u7b54\u304c\u3067\u304d\u308c\u3070\u826f\u304b\u3063\u305f\u3068\u8003\u3048\u3066\u304a\u308a\u307e\u3059\uff0e
\n 
\n2.3. \u611f\u60f3
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u306f\u307e\u3055\u3057\u304f\u8133\u79d1\u5b66\u3068AI\u306e\u540d\u306b\u3075\u3055\u308f\u3057\u304f\uff0cfMRI(functional Magnetic Resonance Imaging)\u3084EEG(Electroencephalogram)\uff0cNIRS(Near Infra-Red Spectroscopy)\u306a\u3069\u591a\u69d8\u306a\u8a08\u6e2c\u6a5f\u5668\u306b\u95a2\u3059\u308b\u7814\u7a76\u304b\u3089\uff0cANN(Artificial Neural Network)\u306a\u3069\u306e\u8133\u306b\u304a\u3051\u308b\u60c5\u5831\u51e6\u7406\u306e\u624b\u7d9a\u304d\u3092\u6a21\u64ec\u3057\u305f\u30d1\u30bf\u30fc\u30f3\u8b58\u5225\u6280\u8853\uff0c\u3068\u304f\u306b\u8fd1\u5e74\u3067\u306fDeep Learning\u306b\u95a2\u3059\u308b\u7814\u7a76\u306a\u3069\u306b\u3064\u3044\u3066\u7740\u76ee\u3057\u305f\u767a\u8868\u304c\u884c\u308f\u308c\u3066\u304a\u308a\uff0c\u975e\u5e38\u306b\u523a\u6fc0\u7684\u3067\u3057\u305f\uff0e
\n\u79c1\u81ea\u8eab\u306e\u4eca\u56de\u306e\u30c6\u30fc\u30de\u306f\u751f\u4f53\u60c5\u5831\u306b\u3088\u308b\u5bfe\u8a71\u578b\u9032\u5316\u8a08\u7b97\u3067\u3059\u304c\uff0c\u6642\u9593\u7684\u306a\u90fd\u5408\u3067\u9032\u5316\u8a08\u7b97\u8981\u7d20\u3092\u542b\u3080\u3053\u3068\u304c\u3067\u304d\u306a\u304b\u3063\u305f\u305f\u3081\uff0c\u751f\u4f53\u60c5\u5831\u306b\u95a2\u3059\u308b\u51e6\u7406\u304c\u767a\u8868\u5185\u5bb9\u306e\u307b\u3068\u3093\u3069\u3092\u5360\u3081\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u305d\u306e\u305f\u3081\uff0c\u3084\u3084\u8cea\u7591\u304c\u932f\u7d9c\u3057\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\uff0c\u305d\u306e\u5f8c\u306e\u61c7\u89aa\u4f1a\u3067\u306f\u5b66\u5916\u306e\u7814\u7a76\u8005\u306e\u65b9\u3068\u306e\u610f\u898b\u4ea4\u63db\u3092\u76db\u3093\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\uff0c\u5927\u5909\u5b9f\u308a\u3042\u308b\u7814\u7a76\u4f1a\u3068\u306a\u3063\u305f\u304b\u3068\u601d\u3044\u307e\u3059\uff0e
\n 
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e4\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u4eba\u306e\u66ae\u3089\u3057\u306b\u95a2\u308f\u308bAI
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u5c71\u53e3\u9ad8\u5e73
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u57fa\u8abf\u8b1b\u6f14
\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Deep QA\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u300c\u30ef\u30c8\u30bd\u30f3\u300d\u304c\uff0c\u7c73\u56fd\u30af\u30a4\u30ba\u756a\u7d44\u300c\u30b8\u30a7\u30d1\u30c7\u30a3\u300d\u306b\u304a\u3044\u3066\uff0c\u4eba\u9593\u306e\u30b0\u30e9\u30f3\u30c9\u30c1\u30e3\u30f3\u30d4\u30aa\u30f3\u306b\u52dd\u5229\u3057\u3066\u4ee5\u6765\uff0c\u306f\u3084\uff12\u5e74\u304c\u7d4c\u904e\u3057\u305f\uff0e\u3053\u306e\u9593\uff0c\u65e5\u7c73\u3067\uff0c\u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u3067\u97f3\u58f0\u30a2\u30b7\u30b9\u30bf\u30f3\u30c8\u30a2\u30d7\u30ea\u304c\u5b9f\u7528\u5316\u3055\u308c\uff0c\u6211\u304c\u56fd\u3067\u306f\uff0c\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240\u306b\u304a\u3044\u3066\uff0c\u300c\u4eba\u5de5\u982d\u8133\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\uff1a\u30ed\u30dc\u30c3\u30c8\u306f\u6771\u5927\u306b\u5165\u308c\u308b\u304b\uff08\u4f1a\u8a8cVol. 27, No. 5\uff09\u300d\u304c\u958b\u59cb\u3055\u308c\uff0cAI\u306b\u5bfe\u3059\u308b\u4e16\u9593\u306e\u95a2\u5fc3\u306f\u9ad8\u304f\u306a\u3063\u3066\u304d\u305f\uff0e\u5b9f\u969b\uff0c\u4f1a\u9577\u5c31\u4efb\u4ee5\u6765\uff0c\u307b\u307c\u6bce\u6708\u306e\u3088\u3046\u306b\u30de\u30b9\u30e1\u30c7\u30a3\u30a2\u304b\u3089\u306e\u53d6\u6750\u3092\u53d7\u3051\uff0cAI\u306e\u958b\u767a\u306e\u5728\u308a\u65b9\uff0c\u4eba\u3068AI\u306e\u4ed8\u304d\u5408\u3044\u65b9\u306a\u3069\uff0c\u591a\u304f\u306e\u8cea\u554f\u3092\u53d7\u3051\uff0cAI\u306b\u5bfe\u3059\u308b\u793e\u4f1a\u306e\u95a2\u5fc3\u306e\u9ad8\u3055\u3092\u5b9f\u611f\u3057\u3066\u3044\u308b\uff0e\u8b1b\u6f14\u8005\u306f\uff0c\u9577\u5e74\uff0c\u77e5\u8b58\u30b7\u30b9\u30c6\u30e0\u306e\u7814\u7a76\u958b\u767a\u306b\u53d6\u308a\u7d44\u307f\uff0c\u305d\u306e\u6709\u7528\u6027\u3068\u9650\u754c\u3092\u611f\u3058\u3066\u304d\u305f\u304c\uff0c\u4eca\u4e00\u5ea6\uff0c\u77e5\u8b58\u578bAI\u306e\u6b74\u53f2\u3092\u632f\u308a\u8fd4\u308a\uff0c\u77e5\u8b58\u30de\u30cd\u30b8\u30e1\u30f3\u30c8\u3084AI\u30b5\u30fc\u30d3\u30b9\u306e\u7814\u7a76\u3092\u4f8b\u306b\u3068\u308a\u306a\u304c\u3089\uff0c\u4eba\u306e\u66ae\u3089\u3057\u306b\u95a2\u308f\u308bAI\u306e\u5728\u308a\u65b9\u306b\u3064\u3044\u3066\u8ff0\u3079\u305f\u3044\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u4f1a\u9577\u3092\u52d9\u3081\u3089\u308c\u308b\u5c71\u53e3\u5148\u751f\u306b\u3088\u308b\u57fa\u8abf\u8b1b\u6f14\u3067\u3059\uff0eAI\u6280\u8853\u306e\u6b74\u53f2\u306b\u3064\u3044\u3066\u8ff0\u3079\u305f\u4e0a\u3067\uff0c\u3053\u308c\u307e\u3067\u306b\u500b\u3005\u306b\u9032\u3081\u3089\u308c\u3066\u304d\u305f\u63a2\u7d22\u985e\u63a8\uff0c\u77e5\u8b58\uff0c\u8a08\u6e2c\u306a\u3069\u306e\u5404\u5206\u91ceAI\u6280\u8853\u306e\u7d71\u5408\u304c\u3044\u3088\u3044\u3088\u76db\u3093\u306b\u306a\u3063\u3066\u304d\u3066\u3044\u308b\u3068\u4ef0\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u5b66\u8853\u5206\u91ce\u3067AI\u6280\u8853\u306b\u6295\u8cc7\u3067\u304d\u308b\u77e5\u8b58(Academic Intelligence, well-defined\u306a\u77e5\u8b58)\u306f\u5f90\u3005\u306b\u53ce\u675f\u3057\u3066\u304a\u308a\uff0c\u4eca\u5f8c\u306f\u5b9f\u8df5\u7684\u77e5\u80fd(Practical Intelligence, ill-defined\u306a\u77e5\u8b58)\u3092\u3069\u3046\u53d6\u308a\u8fbc\u3093\u3067\u3044\u3051\u308b\u304b\u304c\u8ab2\u984c\u3067\u3042\u308b\u3068\u8ff0\u3079\u3066\u3044\u307e\u3057\u305f\uff0e\u305d\u306e\u305f\u3081\u306b\u306f\uff0c\u4eba\u9593\u3068\u4eba\u5de5\u77e5\u80fd\u306e\u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f3\u304c\u91cd\u8981\u3068\u306a\u308a\uff0c\u554f\u984c\u8a8d\u8b58\u2192\u5b9a\u7fa9\u2192\u60c5\u5831\u30a2\u30af\u30bb\u30b9\u2192\u554f\u984c\u89e3\u6c7a\u30fb\u6226\u7565\u7acb\u6848\u2192\u30ea\u30bd\u30fc\u30b9\u5272\u5f53\u2192\u30a2\u30af\u30b7\u30e7\u30f3\u2192\u8a55\u4fa1\u3068\u3044\u3063\u305f\u4e00\u9023\u306e\u30d7\u30ed\u30bb\u30b9\uff08\u30e1\u30bf\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\uff09\u3092\u6301\u3064\u3053\u3068\u304c\u91cd\u8981\u3067\u3042\u308b\u3068\u3044\u3046\u8a71\u3092\u9802\u304d\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Linked Data\u306b\u3088\u308b\u5730\u57df\u60c5\u5831\u3092\u6d3b\u7528\u3057\u305f\u5b66\u8853\u4f1a\u8b70\u652f\u63f4\u30b7\u30b9\u30c6\u30e0
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u677e\u6751 \u51ac\u5b50(\u9752\u5c71\u5b66\u9662\u5927\u5b66 \u7406\u5de5\u5b66\u90e8 \u60c5\u5831\u30c6\u30af\u30ce\u30ed\u30b8\u30fc\u5b66\u79d1)
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a OS-10 Linked Data\u3068\u30aa\u30f3\u30c8\u30ed\u30b8\u30fc-1
\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u5b66\u8853\u4f1a\u8b70\u306e\u53c2\u52a0\u8005\u306f\u4f1a\u8b70\u4e2d\u306e\u6709\u76ca\u306a\u8b70\u8ad6\u3068\u5171\u306b\uff0c\u9650\u3089\u308c\u305f\u4f59\u6687\u306e\u6642\u9593\u306b\u958b\u50ac\u5834\u6240\u306e\u56fd\u3084\u90fd\u5e02\u306e\u6587\u5316\u306a\u3069\u306b\u89e6\u308c\u308b\u6a5f\u4f1a\u3082\u671f\u5f85\u3057\u3066\u3044\u308b\uff0e\u672c\u7a3f\u3067\u306f\uff0c\u767a\u8868\u3084\u4f1a\u5834\u306a\u3069\u306e\u4f1a\u8b70\u306b\u95a2\u3059\u308b\u60c5\u5831\uff0c\u4f1a\u5834\u5468\u8fba\u306e\u89b3\u5149\uff0c\u98f2\u98df\uff0c\u4ea4\u901a\u306a\u3069\u306e\u958b\u50ac\u5834\u6240\u306e\u5730\u57df\u60c5\u5831\u3092Linked Data\u5316\u3057\uff0c\u6709\u610f\u7fa9\u306a\u6ede\u5728\u306e\u305f\u3081\u306e\u60c5\u5831\u63d0\u4f9b\u3092\u884c\u3046\u4f1a\u8b70\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3057\u305f\uff0e\u5b9f\u969b\u306b\u5b66\u8853\u4f1a\u8b70\u3067\u306e\u904b\u7528\u304b\u3089\u5f97\u3089\u308c\u305f\u77e5\u898b\u3088\u308a\uff0c\u5fc5\u8981\u3068\u3055\u308c\u308b\u6a5f\u80fd\u3084\u30c7\u30fc\u30bf\u306a\u3069\u306b\u3064\u3044\u3066\u8b70\u8ad6\u3059\u308b\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u77e5\u7684\u30b7\u30b9\u30c6\u30e0\u30c7\u30b6\u30a4\u30f3\u7814\u7a76\u5ba4\u51fa\u8eab\u306e\u677e\u6751\u3055\u3093\u306b\u3088\u308b\u767a\u8868\u3067\u3059\uff0e\u4f1a\u8b70\u60c5\u5831\u3068\u5730\u57df\u306e\u89b3\u5149\u60c5\u5831\u306e\u53cc\u65b9\u3092RDF\u3067\u8a18\u8ff0\u3057\uff0c\u4e21\u8005\u3092\u5171\u540c\u3067\u5229\u7528\u3059\u308b\u3053\u3068\u3067\uff0c\u4f8b\u3048\u3070\uff0c\u767a\u8868\u306e\u5408\u9593\u306b\u3069\u3053\u306b\u89b3\u5149\u306b\u884c\u304f\uff0c\u3069\u3053\u305d\u3053\u306e\u89b3\u5149\u5730\u306f\u4f55\u6642\u304b\u3089\u4f55\u6642\u307e\u3067\u3084\u3063\u3066\u3044\u308b\uff0c\u4f1a\u8b70\u5834\u304b\u3089\u3069\u3046\u79fb\u52d5\u3059\u308b\u3068\u3044\u3063\u305f\u60c5\u5831\u3092\u4f53\u7cfb\u7684\u306b\u6271\u304a\u3046\u3068\u3044\u3046\u53d6\u308a\u7d44\u307f\u3067\u3059\uff0e\u5143\u3005\u4f1a\u8b70\u60c5\u5831\u306b\u3064\u3044\u3066\u306f\uff0cSemantic Web Conference Ontology\u3068\u3044\u3046\u8a18\u8ff0\u5f62\u5f0f\u304c\u3042\u308a\uff0c\u4eca\u56de\u306f\u305d\u308c\u306b\u89b3\u5149\u5730\u306eLOD\u60c5\u5831\u3092\u878d\u5408\u3055\u305b\u305f\u3082\u306e\u3068\u306a\u308a\u307e\u3059\uff0e\u5b9f\u969b\u306b\uff0cACM Multime-dia2012(ACMMM12)\uff0c\u304a\u3088\u3073The 2nd Joint International Semantic Technology Conference(JIST2012)\u306e2\u3064\u306e\u4f1a\u8b70\u3067\u306e\u4f1a\u8b70\u30b7\u30b9\u30c6\u30e0\u3068\u3057\u3066\u904b\u7528\u3055\u308c\uff0c\u89b3\u5149\u60c5\u5831\u306f\u305d\u306e\u307e\u307e\u306b\uff0c\u4f1a\u8b70\u60c5\u5831\u3092\u5165\u308c\u66ff\u3048\u308b\u3060\u3051\u3067\u5bfe\u5fdc\u3067\u304d\u305f\u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f\u3068\u3044\u3046\u5b9f\u9a13\u7d50\u679c\u3067\u3057\u305f\uff0e\u4eca\u5f8c\u306f\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9\u306e\u5411\u4e0a\u3084\uff0c\u30c7\u30fc\u30bf\u751f\u6210\u306e\u7c21\u6613\u5316\uff0c\u4f1a\u8b70\u306b\u95a2\u3059\u308b\u767a\u8a00\u3092Twitter\u304b\u3089\u5206\u6790\u3059\u308b\u3068\u3044\u3063\u305f\u53d6\u308a\u7d44\u307f\u306b\u3064\u3044\u3066\u691c\u8a0e\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u306e\u3053\u3068\u3067\u3059\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000\u5927\u8133\u76ae\u8cea\u3068Deep Learning\u306e\u985e\u4f3c\u70b9\u3068\u76f8\u9055\u70b9
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u4e00\u6749 \u88d5\u5fd7(\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 \u30d2\u30e5\u30fc\u30de\u30f3\u30e9\u30a4\u30d5\u30c6\u30af\u30ce\u30ed\u30b8\u30fc\u7814\u7a76\u90e8\u9580 \u8133\u6a5f\u80fd\u8a08\u6e2c\u7814\u7a76\u30b0\u30eb\u30fc\u30d7)
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u8133\u79d1\u5b66\u3068AI-3
\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u8133\u304c\u884c\u3063\u3066\u3044\u308b\u60c5\u5831\u51e6\u7406\u3068deep learning\u306e\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u306b\u306f\u5171\u901a\u306e\u7279\u5fb4\u304c\u591a\u304f\u3042\u308b\u304c\uff0c\u8133\u306b\u306f\u73fe\u5728\u306edeep learning\u306b\u306f\u306a\u3044\u91cd\u8981\u306a\u7279\u5fb4\u3082\u3042\u308b\uff0e\u305d\u306e\u4e2d\u306b\u306fdeep learning\u306e\u6027\u80fd\u3092\u3055\u3089\u306b\u5411\u4e0a\u3055\u305b\u308b\u6709\u671b\u306a\u30d2\u30f3\u30c8\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e\uff22\uff25\uff33\uff2f\uff2d\u306f\uff0c\u795e\u7d4c\u79d1\u5b66\u7684\u77e5\u898b\u3092\u3082\u3068\u3065\u304d\uff0c\u5927\u8133\u76ae\u8cea\u306e\u6a5f\u80fd\u3068\u6027\u80fd\u3092\u518d\u73fe\u3055\u305b\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u958b\u767a\u4e2d\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3042\u308b\uff0e\uff22\uff25\uff33\uff2f\uff2d\u306f\u30d9\u30a4\u30b8\u30a2\u30f3\u30cd\u30c3\u30c8\uff0c\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\uff0c\u30b9\u30d1\u30fc\u30b9\u7b26\u53f7\u5316\uff0c\u975e\u7dda\u5f62\uff29\uff23\uff21\u306e\u6a5f\u80fd\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u4e00\u7a2e\u306e\u6559\u5e2b\u306a\u3057\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u3057\u3066\u52d5\u4f5c\u3059\u308b\uff0e\u672c\u8b1b\u6f14\u3067\u306f\uff22\uff25\uff33\uff2f\uff2d\u306e\u6982\u8981\u3068\u6700\u8fd1\u306e\u9032\u5c55\u306b\u3064\u3044\u3066\u8aac\u660e\u3059\u308b\u3068\u3068\u3082\u306b\uff0cdeep learning\u306e\u3088\u3046\u306a\u7279\u5fb4\u62bd\u51fa\u5668\u3068\u3057\u3066\u306e\u5fdc\u7528\u306e\u53ef\u80fd\u6027\u306b\u3064\u3044\u3066\u8ff0\u3079\u308b\uff0e\u307e\u305f\uff0c\u3044\u308f\u3086\u308b\u5f37\u3044\u4eba\u5de5\u77e5\u80fd\u306e\u5b9f\u73fe\u306b\u5411\u3051\u305f\u8ab2\u984c\u306b\u3064\u3044\u3066\u3082\u8b70\u8ad6\u3059\u308b\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u30aa\u30fc\u30ac\u30ca\u30a4\u30ba\u30c9\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u62db\u5f85\u8b1b\u6f14\u3067\uff0c\u7523\u696d\u7dcf\u5408\u7814\u7a76\u6240\u306e\u4e00\u6749\u5148\u751f\u306b\u3088\u308b\uff0c\u5f93\u6765\u306e\u8133\u7814\u7a76\u304b\u3089\u898b\u3048\u3066\u304f\u308b\u8133\u306e\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3068Deep Learning\u306e\u95a2\u4fc2\u6027\u306b\u95a2\u3059\u308b\u3054\u767a\u8868\u3067\u3057\u305f\uff0e\u4e00\u6749\u5148\u751f\u306f\uff0c\u8133\u7814\u7a76\u306b\u307e\u3064\u308f\u308b\u3088\u304f\u3042\u308b\u8aa4\u89e3\u3068\u3057\u3066\u300c\u8133\u306e\u3053\u3068\u306f\u5168\u304f\u5206\u304b\u3063\u3066\u3044\u306a\u3044\u300d\uff0c\u300c\u8133\u3068\u8a08\u7b97\u6a5f\u3068\u306f\u5168\u304f\u9055\u3046\u60c5\u5831\u51e6\u7406\u3092\u3057\u3066\u3044\u308b\u300d\uff0c\u300c\u8133\u306f\u3068\u3066\u3082\u8907\u96d1\u306a\u7d44\u7e54\u3067\u3042\u308b\u300d\uff0c\u300c\u8a08\u7b97\u91cf\u304c\u81a8\u5927\u3059\u304e\u3066\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3067\u304d\u306a\u3044\u300d\u3068\u3044\u3063\u305f\u70b9\u3092\u3054\u6307\u6458\u3055\u308c\uff0c\u305d\u306e\u4e0a\u3067\u660e\u3089\u304b\u306b\u306a\u308a\u3064\u3064\u3042\u308b\u8133\u306e\u5b66\u7fd2\u30e1\u30ab\u30cb\u30ba\u30e0\u3068Deep Learning\u306e\u968e\u5c64\u69cb\u9020\u304c\uff0c\u5b66\u7fd2\u3092\u9032\u3081\u308b\u4ed5\u7d44\u307f\u3068\u3057\u3066\u3088\u304f\u4f3c\u3066\u3044\u308b\u3068\u3044\u3046\u70b9\u306b\u3064\u3044\u3066\u3054\u8aac\u660e\u3055\u308c\u307e\u3057\u305f\uff0e\u305d\u306e\u4e0a\u3067\uff0cDeep Learning\u306b\u306f\u306a\u3044\u8133\u306e\u7279\u5fb4\u3092\u7528\u3044\u308b\u3053\u3068\u3067\uff0c\u3055\u3089\u306b\u6027\u80fd\u306e\u5411\u4e0a\u304c\u306f\u304b\u308c\u308b\u306e\u3067\u306f\u306a\u3044\u304b\uff0c\u3068\u3044\u3046\u3088\u3046\u306a\u5c55\u671b\u306b\u3064\u3044\u3066\u8ff0\u3079\u3066\u3044\u3089\u3063\u3057\u3083\u3044\u307e\u3057\u305f\uff0e\u975e\u5e38\u306b\u8208\u5473\u6df1\u304f\uff0c\u523a\u6fc0\u7684\u306a\u5185\u5bb9\u3067\u3042\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u8133\u306e\u63a8\u8ad6\u6a5f\u69cb\u3068\u985e\u4f3c\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u30d9\u30a4\u30b8\u30a2\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\uff0c\u8133\u306e\u795e\u7d4c\u767a\u706b\u3092\u6a21\u64ec\u3057\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u672c\u7814\u7a76\u5ba4\u3067\u3082\u304d\u3061\u3093\u3068\u52c9\u5f37\u3057\u3066\u304a\u304f\u3079\u304d\u4e8b\u9805\u3067\u3042\u308b\u3068\u6539\u3081\u3066\u5f37\u304f\u601d\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000\u7570\u5206\u91ce\u5171\u540c\u7814\u7a76\u5c65\u6b74\u5206\u6790\u306e\u4e8b\u4f8b
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u7530\u4e2d \u514b\u660e(\u4e00\u6a4b\u5927\u5b66\u60c5\u5831\u57fa\u76e4\u30bb\u30f3\u30bf\u30fc)\uff0c\u6ff1\u5d0e \u96c5\u5f18(\u72ec\u7acb\u884c\u653f\u6cd5\u4eba\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240\u60c5\u5831\u6280\u8853\u7814\u7a76\u90e8\u9580)
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u77e5\u8b58\u306e\u5229\u7528\u3068\u5171\u6709-2
\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u60c5\u5831\u30c7\u30b6\u30a4\u30f3\uff0c\u30bd\u30b7\u30aa\u30fb\u30e1\u30c7\u30a3\u30a2\u8ad6\uff0c\u5b9f\u4e16\u754c\u6307\u5411\u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f3\uff0c\u4eba\u5de5\u77e5\u80fd\u3068\u3044\u3063\u305f\u7570\u306a\u308b\u5206\u91ce\u306e\u7814\u7a76\u8005\u304c\u96c6\u307e\u308a\uff0c\u3044\u304f\u3064\u3082\u306e\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u3092\u5b9f\u65bd\u3057\u306a\u304c\u3089\u7570\u5206\u91ce\u5171\u540c\u306b\u3088\u308b\u7814\u7a76\u3092\u9032\u3081\u305f\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u3064\u3044\u3066\uff0c\u30e1\u30fc\u30eb\u306a\u3069\u6587\u66f8\u3068\u3057\u3066\u6b8b\u3055\u308c\u305f\u8a18\u9332\u3092\u4e2d\u5fc3\u306b\uff0c\u6d3b\u52d5\u5c65\u6b74\u306e\u5206\u6790\u3092\u884c\u3046\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u77e5\u7684\u30b7\u30b9\u30c6\u30e0\u30c7\u30b6\u30a4\u30f3\u7814\u7a76\u5ba4\u306e\u51fa\u8eab\u3067\u3042\u308b\u6ff1\u5d0e\u3055\u3093\u306e\u3054\u767a\u8868\u3067\u3059\uff0e\u4f8b\u3048\u3070\u82b8\u8853\u5927\u5b66\u306a\u3069\u5168\u304f\u7570\u5206\u91ce\u306e\u65b9\u3068\u5171\u540c\u4f5c\u696d\u3059\u308b\u306b\u3042\u305f\u3063\u3066\uff0c\u80cc\u666f\u77e5\u8b58\u3084\u6587\u5316\u306e\u9055\u3044\u306a\u3069\u304b\u3089\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u304c\u4e0a\u624b\u304f\u884c\u304b\u305a\uff0c\u4e92\u3044\u304c\u300c\u671f\u5f85\u300d\u3057\u3066\u3044\u308b\u3053\u3068\u3068\uff0c\u5b9f\u969b\u306b\u300c\u884c\u52d5\u300d\u3059\u308b\u3053\u3068\u306b\u5dee\u304c\u751f\u3058\u3066\u3057\u307e\u3046\u3068\u3044\u3063\u305f\u554f\u984c\u304c\u3042\u308a\u307e\u3059\uff0e\u305d\u308c\u3089\u306e\u65b9\u5411\u6027\u306e\u9055\u3044\u3092\uff0c\u3084\u308a\u3068\u308a\u3059\u308b\u30e1\u30fc\u30eb\u304b\u3089\u78ba\u7387\u7684\u6f5c\u5728\u610f\u5473\u89e3\u6790(Probabilistic Latent Semantic Analysis: PLSA)\u306b\u3088\u3063\u3066\u89e3\u6790\u3057\uff0c\u6642\u7cfb\u5217\u3067\u53ef\u8996\u5316\u3059\u308b\u3053\u3068\u3067\uff0c\u3069\u3053\u3067\u98df\u3044\u9055\u3044\u304c\u751f\u3058\u3066\u3044\u308b\u304b\u306b\u3064\u3044\u3066\u30e6\u30fc\u30b6\u306b\u5448\u793a\u3059\u308b\u3068\u3044\u3046\u30b7\u30b9\u30c6\u30e0\u306e\u3054\u63d0\u6848\u3067\u3057\u305f\uff0e\u7570\u5206\u91ce\u3060\u3068\u8a00\u8449\u306e\u610f\u5473\u3084\u4f7f\u308f\u308c\u65b9\u304c\u7570\u306a\u308b\u305f\u3081\uff0c\u610f\u5473\u89e3\u6790\u306e\u30b3\u30fc\u30d1\u30b9\u304c\u3076\u308c\u3066\u96e3\u3057\u305d\u3046\u3060\u3068\u306f\u611f\u3058\u307e\u3057\u305f\u304c\uff0c\u7570\u5206\u91ce\u9593\u3067\u306e\u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f3\u3092\u5982\u4f55\u306b\u652f\u63f4\u3059\u308b\u304b\u306b\u3064\u3044\u3066\u306e\u53d6\u308a\u7d44\u307f\u3068\u3057\u3066\uff0c\u975e\u5e38\u306b\u9762\u767d\u3044\u767a\u8868\u3067\u3042\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u500b\u4eba\u7684\u306b\u306f\u300c\u5b66\u751f\u3068\u3044\u308d\u3044\u308d\u3084\u308b\u306e\u3082\u7570\u5206\u91ce\u5171\u540c\u4f5c\u696d\u3060\u300d\u3068\u3044\u3046\u30b3\u30e1\u30f3\u30c8\u304c\u79c0\u9038\u3067\u3057\u305f\uff0e
\n 
\n\u53c2\u8003\u6587\u732e
\n1)\u00a0\u00a0\u00a0 2013\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a(\u7b2c27\u56de)
\nhttp:\/\/2013.conf.ai-gakkai.or.jp\/
\n <\/p>\n","protected":false},"excerpt":{"rendered":"

\u795e\u6238\u3067\u958b\u50ac\u3055\u308c\u305f SCIS-ISIS 2012 (The 6th International Conference on Soft Computing and Intelligent Systems The 13th I … <\/p>\n

“SCIS-ISIS 2012” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10,3],"tags":[],"class_list":["post-1213","post","type-post","status-publish","format-standard","hentry","category-10","category-3"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/1213","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1213"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/1213\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1213"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1213"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}