{"id":"https://openalex.org/W2975706231","doi":"https://doi.org/10.1109/cig.2019.8848090","title":"Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning","display_name":"Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning","publication_year":2019,"publication_date":"2019-08-01","ids":{"openalex":"https://openalex.org/W2975706231","doi":"https://doi.org/10.1109/cig.2019.8848090","mag":"2975706231"},"language":"en","primary_location":{"id":"doi:10.1109/cig.2019.8848090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2019.8848090","pdf_url":null,"source":{"id":"https://openalex.org/S4306498491","display_name":"2019 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053661278","display_name":"SangGyu Nam","orcid":"https://orcid.org/0000-0002-7424-8469"},"institutions":[{"id":"https://openalex.org/I177738480","display_name":"Japan Advanced Institute of Science and Technology","ror":"https://ror.org/03frj4r98","country_code":"JP","type":"education","lineage":["https://openalex.org/I177738480"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"SangGyu Nam","raw_affiliation_strings":["School of Information Science, JAIST, Ishikawa, Japan"],"affiliations":[{"raw_affiliation_string":"School of Information Science, JAIST, Ishikawa, Japan","institution_ids":["https://openalex.org/I177738480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105422798","display_name":"Kokolo Ikeda","orcid":null},"institutions":[{"id":"https://openalex.org/I177738480","display_name":"Japan Advanced Institute of Science and Technology","ror":"https://ror.org/03frj4r98","country_code":"JP","type":"education","lineage":["https://openalex.org/I177738480"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kokolo Ikeda","raw_affiliation_strings":["School of Information Science, JAIST, Ishikawa, Japan"],"affiliations":[{"raw_affiliation_string":"School of Information Science, JAIST, Ishikawa, Japan","institution_ids":["https://openalex.org/I177738480"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053661278"],"corresponding_institution_ids":["https://openalex.org/I177738480"],"apc_list":null,"apc_paid":null,"fwci":1.1807,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84335904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11197","display_name":"Digital Games and Media","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8563823699951172},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7752473950386047},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6890888810157776},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6252347826957703},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5747905969619751},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5106869339942932},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48719924688339233},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4838046431541443},{"id":"https://openalex.org/keywords/function-approximation","display_name":"Function approximation","score":0.44758573174476624},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39761173725128174}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8563823699951172},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752473950386047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6890888810157776},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6252347826957703},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5747905969619751},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5106869339942932},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48719924688339233},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4838046431541443},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.44758573174476624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39761173725128174},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cig.2019.8848090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2019.8848090","pdf_url":null,"source":{"id":"https://openalex.org/S4306498491","display_name":"2019 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W181183076","https://openalex.org/W850812170","https://openalex.org/W1959608418","https://openalex.org/W2099471712","https://openalex.org/W2145339207","https://openalex.org/W2168115594","https://openalex.org/W2170567160","https://openalex.org/W2170724701","https://openalex.org/W2173248099","https://openalex.org/W2290393232","https://openalex.org/W2423557781","https://openalex.org/W2532584108","https://openalex.org/W2591027307","https://openalex.org/W2951438386","https://openalex.org/W2963636093","https://openalex.org/W2963690854","https://openalex.org/W2964201809","https://openalex.org/W2964223825","https://openalex.org/W3106287232","https://openalex.org/W4288359814","https://openalex.org/W4289492459","https://openalex.org/W4311415873","https://openalex.org/W4320013936","https://openalex.org/W6607402785","https://openalex.org/W6640963894","https://openalex.org/W6684921986","https://openalex.org/W6696872630","https://openalex.org/W6750998844","https://openalex.org/W6754645375"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3021988786","https://openalex.org/W2380199044","https://openalex.org/W3099311996","https://openalex.org/W2963971282"],"abstract_inverted_index":{"In":[0,106],"this":[1],"study,":[2],"procedural":[3],"content":[4,21,46,85],"generation":[5,18,45],"(PCG)":[6],"using":[7,28],"reinforcement":[8],"learning":[9],"(RL)":[10],"is":[11,14,32,90,96,113,132,143],"focused.":[12],"PCG":[13,38],"defined":[15,25],"as":[16,49,59,101,115,173],"the":[17,24,35,73,78,84,108,140,150,168,183,186,201,205],"of":[19,34,37,80,110,185,208],"game":[20,74],"tailored":[22],"to":[23,43,72,98,135,144],"evaluation":[26,152],"function":[27],"RL":[29,95,155],"models,":[30,156],"which":[31],"one":[33],"examples":[36],"via":[39],"machine":[40],"learning.":[41],"Compared":[42],"other":[44],"areas":[47],"such":[48,58],"computer":[50],"vision":[51],"and":[52,63,124,139,159,167,175,195,204],"natural":[53],"language":[54],"process,":[55],"generative":[56,64],"models":[57],"variational":[60],"autoencoders,":[61],"PixelCNN,":[62],"adversarial":[65],"networks":[66],"exhibit":[67],"some":[68],"difficulties":[69],"for":[70,88,104],"applications":[71],"area":[75],"because":[76,119],"during":[77],"development":[79],"a":[81,102,133],"new":[82],"game,":[83],"data":[86],"used":[87,100],"training":[89],"typically":[91],"not":[92],"sufficient.":[93],"Hence,":[94],"considered":[97],"be":[99],"method":[103],"PCG.":[105],"particular,":[107],"stage":[109],"turn-based":[111],"RPG":[112],"selected":[114],"our":[116,178],"research":[117],"target":[118],"it":[120,131],"comprises":[121],"discrete":[122],"sections,":[123],"its":[125],"parameters":[126],"were":[127],"closely":[128],"related;":[129],"hence,":[130],"challenge":[134],"generate":[136,145],"desirable":[137],"stages,":[138],"main":[141],"goal":[142],"various":[146],"stages":[147,170,191],"guided":[148],"by":[149,177,200],"designed":[151,179],"function.":[153],"Two":[154],"Deep":[157,160],"Q-Network":[158],"Deterministic":[161],"Policy":[162],"Gradient,":[163],"respectively,":[164],"are":[165,171,192,198],"selected,":[166],"generated":[169],"evaluated":[172,199],"0.78":[174],"0.85":[176],"function,":[180],"respectively.":[181],"By":[182],"application":[184],"stochastic":[187],"noise":[188],"policy,":[189],"diverse":[190],"successfully":[193],"obtained,":[194],"those":[196],"diversities":[197],"parameter":[202],"mse":[203],"different":[206],"number":[207],"valid":[209],"strategies.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
