{"id":"https://openalex.org/W4385484533","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191629","title":"Attentional Opponent Modelling for Multi-agent Cooperation","display_name":"Attentional Opponent Modelling for Multi-agent Cooperation","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385484533","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191629"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102541124","display_name":"Siyang Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117754","display_name":"Chinese Academy of Civil Aviation Science and Technology","ror":"https://ror.org/023zynq23","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210117754"]},{"id":"https://openalex.org/I4387152784","display_name":"Chinese Aeronautical Establishment","ror":"https://ror.org/01qwh7a74","country_code":"CN","type":"government","lineage":["https://openalex.org/I4387152784"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyang Tan","raw_affiliation_strings":["Chinese Aeronautical Establishment,Beijing,China","Chinese Aeronautical Establishment, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Aeronautical Establishment,Beijing,China","institution_ids":["https://openalex.org/I4210117754","https://openalex.org/I4387152784"]},{"raw_affiliation_string":"Chinese Aeronautical Establishment, Beijing, China","institution_ids":["https://openalex.org/I4210117754","https://openalex.org/I4387152784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101730540","display_name":"Binqiang Chen","orcid":"https://orcid.org/0000-0001-7949-3191"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binqiang Chen","raw_affiliation_strings":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2178,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42165621,"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":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9986000061035156,"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":0.9986000061035156,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9865000247955322,"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/T11252","display_name":"Evolutionary Game Theory and Cooperation","score":0.982200026512146,"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/computer-science","display_name":"Computer science","score":0.8037427663803101},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7797470092773438},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.6644886136054993},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6503325700759888},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5430009961128235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4926806092262268},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4513973593711853},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.36965569853782654},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.14263001084327698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8037427663803101},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7797470092773438},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.6644886136054993},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6503325700759888},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5430009961128235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4926806092262268},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4513973593711853},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36965569853782654},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.14263001084327698},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1484113995","https://openalex.org/W1515851193","https://openalex.org/W1676755748","https://openalex.org/W2012812921","https://openalex.org/W2088956500","https://openalex.org/W2133564696","https://openalex.org/W2144240978","https://openalex.org/W2145339207","https://openalex.org/W2149551746","https://openalex.org/W2156194062","https://openalex.org/W2396783599","https://openalex.org/W2475089067","https://openalex.org/W2756196406","https://openalex.org/W2758442112","https://openalex.org/W2803155336","https://openalex.org/W2803926500","https://openalex.org/W2807741983","https://openalex.org/W2810602713","https://openalex.org/W2915117209","https://openalex.org/W2950397026","https://openalex.org/W2963091558","https://openalex.org/W2963864421","https://openalex.org/W2964251366","https://openalex.org/W2964338167","https://openalex.org/W2971727319","https://openalex.org/W2981038142","https://openalex.org/W2997070234","https://openalex.org/W2998489261","https://openalex.org/W3099518626","https://openalex.org/W3100930738","https://openalex.org/W3209862138","https://openalex.org/W4285723986","https://openalex.org/W4287756582","https://openalex.org/W4288594419","https://openalex.org/W4295150809","https://openalex.org/W4295598622","https://openalex.org/W4297627396","https://openalex.org/W4299802797","https://openalex.org/W4302570325","https://openalex.org/W4385245566","https://openalex.org/W6628758712","https://openalex.org/W6679434410","https://openalex.org/W6684205842","https://openalex.org/W6684921986","https://openalex.org/W6713411898","https://openalex.org/W6721101288","https://openalex.org/W6738796088","https://openalex.org/W6739901393","https://openalex.org/W6744537943","https://openalex.org/W6744562401","https://openalex.org/W6748203849","https://openalex.org/W6748910126","https://openalex.org/W6749304979","https://openalex.org/W6751139674","https://openalex.org/W6751912414","https://openalex.org/W6752380930","https://openalex.org/W6758507868","https://openalex.org/W6758846586","https://openalex.org/W6767328439","https://openalex.org/W6771274123","https://openalex.org/W6779753894","https://openalex.org/W6785871430","https://openalex.org/W6803581605"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W4388150944","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"Opponent":[0],"modelling":[1,76],"(OM)":[2],"can":[3,97],"enable":[4],"agents":[5],"to":[6,53,60,78,115],"reason":[7],"behaviors":[8],"of":[9,30,82,137],"others,":[10],"and":[11,15,56,96,111,129],"hence":[12],"act":[13],"accordingly":[14],"interact":[16],"effectively":[17],"in":[18,40],"multi-agent":[19,117],"reinforcement":[20],"learning":[21],"(MARL).":[22],"Existing":[23],"OM":[24,143],"approaches":[25],"commonly":[26],"assume":[27],"the":[28,41,80,134],"availability":[29],"opponents'":[31],"trajectory,":[32],"which":[33],"needs":[34],"incessant":[35],"message":[36],"exchange":[37],"during":[38,94],"execution":[39,95],"partially":[42],"observable":[43],"environment.":[44],"As":[45,105],"a":[46],"result,":[47],"they":[48],"are":[49,57],"not":[50,91],"cost-effective":[51],"due":[52],"communication":[54,93],"overhead,":[55],"also":[58],"inapplicable":[59],"many":[61],"practical":[62],"tasks":[63,124],"with":[64,85,101,141],"constraints":[65],"on":[66,121],"communication.":[67],"To":[68],"handle":[69],"this":[70],"problem,":[71],"we":[72,107],"propose":[73,108],"attentional":[74],"opponent":[75],"(ATOM)":[77],"infer":[79],"beliefs":[81],"neighboring":[83],"entities":[84],"only":[86],"local":[87],"observation.":[88],"ATOM":[89,109,112],"does":[90],"require":[92],"be":[98],"easily":[99],"combined":[100],"existing":[102],"MARL":[103],"methods.":[104],"examples,":[106],"Actor-Critic":[110],"Q-learning":[113],"architectures":[114],"facilitate":[116],"cooperation.":[118],"Extensive":[119],"experiments":[120],"several":[122],"challenging":[123],"(i.e.,":[125],"Cooperative":[126],"Navigation,":[127],"Predator-Prey":[128],"Starcraft":[130],"Multi-Agent":[131],"Challenge)":[132],"show":[133],"superior":[135],"performance":[136],"our":[138],"methods":[139],"compared":[140],"benchmark":[142],"approaches.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
