{"id":"https://openalex.org/W4225640070","doi":"https://doi.org/10.1145/3512576.3512596","title":"RTS Game AI Robots Winner Prediction Based on Replay Data by using Deep Learning","display_name":"RTS Game AI Robots Winner Prediction Based on Replay Data by using Deep Learning","publication_year":2021,"publication_date":"2021-12-22","ids":{"openalex":"https://openalex.org/W4225640070","doi":"https://doi.org/10.1145/3512576.3512596"},"language":"en","primary_location":{"id":"doi:10.1145/3512576.3512596","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512576.3512596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 9th International Conference on Information Technology: IoT and Smart City","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/A5103224423","display_name":"Huifen Li","orcid":"https://orcid.org/0000-0002-8548-3042"},"institutions":[{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Huifen Li","raw_affiliation_strings":["School of Computer Engineering, Guangzhou City University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Guangzhou City University of Technology, China","institution_ids":["https://openalex.org/I6469544"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101853761","display_name":"Jie Zhou","orcid":"https://orcid.org/0009-0004-3384-0556"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zhou","raw_affiliation_strings":["School of Computer Science&amp;Engineering, South China University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science&amp;Engineering, South China University of Technology, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100699816","display_name":"Yu Tian","orcid":"https://orcid.org/0000-0002-8235-6507"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Yu","raw_affiliation_strings":["School of Computer Science&amp;Engineering, South China University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science&amp;Engineering, South China University of Technology, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103224423"],"corresponding_institution_ids":["https://openalex.org/I6469544"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.21298348,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.991599977016449,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.991599977016449,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.9882000088691711,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9736999869346619,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7797974348068237},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6165643334388733},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6035546660423279},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4438864290714264},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4336734414100647}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797974348068237},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6165643334388733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6035546660423279},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4438864290714264},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4336734414100647}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3512576.3512596","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512576.3512596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 9th International Conference on Information Technology: IoT and Smart City","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":23,"referenced_works":["https://openalex.org/W2049165798","https://openalex.org/W2064675550","https://openalex.org/W2112796928","https://openalex.org/W2146611287","https://openalex.org/W2194775991","https://openalex.org/W2230033167","https://openalex.org/W2257979135","https://openalex.org/W2402710308","https://openalex.org/W2589422499","https://openalex.org/W2766447205","https://openalex.org/W2892899631","https://openalex.org/W2896247463","https://openalex.org/W2900716645","https://openalex.org/W2904907152","https://openalex.org/W2919115771","https://openalex.org/W2951404535","https://openalex.org/W2964980087","https://openalex.org/W2982316857","https://openalex.org/W3037839749","https://openalex.org/W4299335686","https://openalex.org/W6639006556","https://openalex.org/W6752040014","https://openalex.org/W6764214684"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"The":[0,23,154,190],"success":[1],"of":[2,6,14,25,64,95,118,187,222,231],"the":[3,12,87,93,100,116,136,193,217,220,229],"AlphaGo":[4],"series":[5],"artificial":[7],"intelligence":[8],"(AI)":[9],"programs":[10],"in":[11,115],"game":[13,55,88],"Go":[15],"has":[16],"injected":[17],"new":[18,177],"vitality":[19],"into":[20],"AI":[21,39,56],"research.":[22,46],"research":[24],"real-time":[26],"strategy":[27],"(RTS)":[28],"games":[29],"using":[30,72,175],"deep":[31,73,145,161],"learning":[32,146,162],"algorithms":[33],"provides":[34],"an":[35,166],"ideal":[36],"method":[37],"for":[38,53],"planning,":[40],"state":[41],"evaluations,":[42],"and":[43,61,92,112,123,142,152],"human":[44],"behavior":[45],"Our":[47],"work":[48],"focused":[49],"on":[50,59,179],"winner":[51,77,131],"prediction":[52,78,96,132],"RTS":[54],"robots":[57],"based":[58],"states":[60,82],"actions":[62,85],"information":[63,83,89],"replay":[65,225],"dataset":[66],"generated":[67],"by":[68,71],"\u03bcRTS":[69],"simulator":[70],"learning.":[74],"Since":[75],"previous":[76,188],"methods":[79,163],"only":[80],"encoded":[81,137],"without":[84],"information,":[86],"is":[90,97],"incomplete":[91],"accuracy":[94,167,186],"low.":[98],"In":[99],"present":[101],"work,":[102],"we":[103,211],"used":[104,135],"one-hot":[105],"encoding":[106,178],"to":[107,139,219,227],"encode":[108],"both":[109],"\u201cstates\u201d":[110],"elements":[111,114],"\u201cactions\u201d":[113],"attributes":[117],"each":[119,223],"sampled":[120,212],"time":[121,214],"point":[122],"corresponding":[124],"\u201cwinner\u201d":[125],"attribute,":[126],"which":[127],"can":[128,164,201],"achieve":[129,165,202],"higher":[130],"performance.":[133],"We":[134],"datasets":[138],"train":[140],"(validate)":[141],"test":[143],"five":[144,160],"algorithms:":[147],"CNN,":[148],"MSCNN,":[149],"CNP,":[150],"LSTM,":[151],"BNN.":[153],"experimental":[155],"results":[156],"showed":[157],"that":[158],"all":[159],"greater":[168,172,203,207],"than":[169,173,204,208],"77%":[170],"(often":[171,206],"80%)":[174],"this":[176],"a":[180],"task":[181],"comparing":[182],"with":[183],"around":[184],"60%":[185],"algorithms.":[189],"Area":[191],"Under":[192],"Receiver":[194],"Operating":[195],"Characteristic":[196],"(ROC)":[197],"Curve":[198],"(AUC)":[199],"value":[200],"0.85":[205],"0.9).":[209],"Finally,":[210],"20":[213],"points":[215],"from":[216],"beginning":[218],"end":[221],"match's":[224],"data":[226],"illustrate":[228],"feasibility":[230],"our":[232],"methods.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
