{"id":"https://openalex.org/W4205721886","doi":"https://doi.org/10.1109/cog52621.2021.9619111","title":"GomokuNet: A Novel UNet-style Network for Gomoku Zero Learning via Exploiting Positional Information and Multiscale Features","display_name":"GomokuNet: A Novel UNet-style Network for Gomoku Zero Learning via Exploiting Positional Information and Multiscale Features","publication_year":2021,"publication_date":"2021-08-17","ids":{"openalex":"https://openalex.org/W4205721886","doi":"https://doi.org/10.1109/cog52621.2021.9619111"},"language":"en","primary_location":{"id":"doi:10.1109/cog52621.2021.9619111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog52621.2021.9619111","pdf_url":null,"source":{"id":"https://openalex.org/S4363608335","display_name":"2021 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":"2021 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/A5045927598","display_name":"Yifan Gao","orcid":"https://orcid.org/0000-0002-9184-0085"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yifan Gao","raw_affiliation_strings":["Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074858946","display_name":"Lezhou Wu","orcid":"https://orcid.org/0000-0002-9432-9573"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lezhou Wu","raw_affiliation_strings":["Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007533272","display_name":"Haoyue Li","orcid":"https://orcid.org/0000-0001-5284-9661"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyue Li","raw_affiliation_strings":["Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045927598"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.8796,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77949149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9944000244140625,"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":0.9944000244140625,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9869999885559082,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9839000105857849,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.7548207640647888},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7069393992424011},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6657221913337708},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.591184675693512},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5581430792808533},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48086100816726685},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3732123374938965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3564092516899109}],"concepts":[{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.7548207640647888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7069393992424011},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6657221913337708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.591184675693512},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5581430792808533},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48086100816726685},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3732123374938965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3564092516899109},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/cog52621.2021.9619111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog52621.2021.9619111","pdf_url":null,"source":{"id":"https://openalex.org/S4363608335","display_name":"2021 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":"2021 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":13,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2576264401","https://openalex.org/W2766447205","https://openalex.org/W2902907165","https://openalex.org/W2918318309","https://openalex.org/W2921168138","https://openalex.org/W2950137412","https://openalex.org/W2967063544","https://openalex.org/W3081484978","https://openalex.org/W3109537774","https://openalex.org/W6758872754"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2142795561","https://openalex.org/W2611989081","https://openalex.org/W3155418658","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Since":[0],"the":[1,5,18,39,79,84,102,106,125,131],"tremendous":[2],"success":[3],"of":[4,41,86,105],"AlphaGo":[6,9],"family":[7],"(AlphaGo,":[8],"Zero,":[10],"and":[11,28,51,57,138],"Alpha":[12],"Zero),":[13],"zero":[14,33,46,121,135],"learning":[15,34,47,122,136],"has":[16],"become":[17],"baseline":[19],"method":[20,129],"for":[21,70],"many":[22],"board":[23],"games,":[24],"such":[25],"as":[26,78],"Gomoku":[27,71],"Chess.":[29],"Recent":[30],"works":[31],"on":[32,124],"have":[35],"demonstrated":[36],"that":[37,115],"improving":[38],"performance":[40],"neural":[42],"networks":[43,123],"used":[44],"in":[45,97],"programs":[48],"remains":[49],"nontrivial":[50],"challenging.":[52],"Considering":[53],"both":[54],"positional":[55,65],"information":[56,90,104],"multiscale":[58,87],"features,":[59],"this":[60],"paper":[61],"presents":[62],"a":[63],"novel":[64],"attention-based":[66],"UNet-style":[67],"model":[68,96],"(GomokuNet)":[69],"AI.":[72],"An":[73],"encoder-decoder":[74],"architecture":[75],"is":[76],"adopted":[77],"backbone":[80],"network":[81],"to":[82,99,133],"guarantee":[83],"fusion":[85],"features.":[88],"Positional":[89],"modules":[91],"are":[92],"incorporated":[93],"into":[94],"our":[95,116],"order":[98],"further":[100],"capture":[101],"location":[103],"board.":[107],"Quantitative":[108],"results":[109],"obtained":[110],"by":[111],"ablation":[112],"analysis":[113],"indicate":[114],"GomokuNet":[117],"outperforms":[118],"previous":[119],"state-of-the-art":[120],"RenjuNet":[126],"dataset.":[127],"Our":[128],"shows":[130],"potential":[132],"improve":[134],"efficiency":[137],"AI":[139],"engine":[140],"performance.":[141]},"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":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}