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Considering the ways actual coaches of soccer enable their players learn to execute the soccer tactics, we developed a method of agents\u0027 learning to distinguish good tactics from not-so-good tactics. It is made up mainly of small practical tasks requiring a few agents, of acquisition of appropriate cognitive maps by decomposing the situations into grid information, and of optimization of total play by a kind of adaptive learning. Because the agents perceive the environment as a grid, they have a finite number of condition spaces and are able to predict the behavior of opponents by learning the conditonal probabilities. Each condition has its own utility learned in an evolutionary method.", "subitem_description_type": "Abstract"}]}, "item_2_full_name_3": {"attribute_name": "\u8457\u8005\u5225\u540d", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "105948", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Kumada, Yoichiro"}]}, {"nameIdentifiers": [{"nameIdentifier": "105949", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Ueda, Kazuhiro"}]}]}, "item_2_publisher_20": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a"}]}, "item_2_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "info:doi/10.1527/tjsai.16.120", "subitem_relation_type_select": "DOI"}}]}, "item_2_source_id_10": {"attribute_name": "\u66f8\u8a8c\u30ec\u30b3\u30fc\u30c9ID", "attribute_value_mlt": [{"subitem_source_identifier": "AA11579226", "subitem_source_identifier_type": "NCID"}]}, "item_2_source_id_8": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "13460714", "subitem_source_identifier_type": "ISSN"}]}, "item_2_subject_15": {"attribute_name": "\u65e5\u672c\u5341\u9032\u5206\u985e\u6cd5", "attribute_value_mlt": [{"subitem_subject": "007.13", "subitem_subject_scheme": "NDC"}]}, "item_2_text_34": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_text_value": "Journal Article"}]}, "item_2_text_4": {"attribute_name": "\u8457\u8005\u6240\u5c5e", "attribute_value_mlt": [{"subitem_text_value": "\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u7dcf\u5408\u6587\u5316\u7814\u7a76\u79d1\u5e83\u57df\u79d1\u5b66\u5c02\u653b"}, {"subitem_text_value": "Department of Systems Science, The University of Tokyo"}, {"subitem_text_value": "\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u5b66\u74b0\u30fb\u5b66\u969b\u60c5\u5831\u5b66\u5e9c"}, {"subitem_text_value": "Interfaculty Initiative in Information Studies, The University of Tokyo"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u718a\u7530, \u967d\u4e00\u90ce"}], "nameIdentifiers": [{"nameIdentifier": "105946", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u690d\u7530, \u4e00\u535a"}], "nameIdentifiers": [{"nameIdentifier": "105947", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2017-06-26"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "AI_16_01.pdf", "filesize": [{"value": "2.8 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 2800000.0, "url": {"label": "AI_16_01.pdf", "url": "https://repository.dl.itc.u-tokyo.ac.jp/record/183/files/AI_16_01.pdf"}, "version_id": "1bab0ff8-2277-4853-afff-38836d6f0f66"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "soccer agents", "subitem_subject_scheme": "Other"}, {"subitem_subject": "cognitive modeling", "subitem_subject_scheme": "Other"}, {"subitem_subject": "cooperative tactics", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Bayesian prediction", "subitem_subject_scheme": "Other"}, {"subitem_subject": "adaptive learning", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "\u4e88\u6e2c\u80fd\u529b\u3092\u6301\u3064\u30b5\u30c3\u30ab\u30fc\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u3088\u308b\u5354\u8abf\u6226\u8853\u306e\u7372\u5f97", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "\u4e88\u6e2c\u80fd\u529b\u3092\u6301\u3064\u30b5\u30c3\u30ab\u30fc\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u3088\u308b\u5354\u8abf\u6226\u8853\u306e\u7372\u5f97"}]}, "item_type_id": "2", "owner": "1", "path": ["9/10/15", "67/68/69"], "permalink_uri": "http://hdl.handle.net/2261/8105", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2007-12-27"}, "publish_date": "2007-12-27", "publish_status": "0", "recid": "183", "relation": {}, "relation_version_is_last": true, "title": ["\u4e88\u6e2c\u80fd\u529b\u3092\u6301\u3064\u30b5\u30c3\u30ab\u30fc\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u3088\u308b\u5354\u8abf\u6226\u8853\u306e\u7372\u5f97"], "weko_shared_id": null}