{"id":"https://openalex.org/W4247855592","doi":"https://doi.org/10.1109/wsc.2016.7822130","title":"AlphaGo and Monte Carlo tree search: The simulation optimization perspective","display_name":"AlphaGo and Monte Carlo tree search: The simulation optimization perspective","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W4247855592","doi":"https://doi.org/10.1109/wsc.2016.7822130"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2016.7822130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2016.7822130","pdf_url":null,"source":{"id":"https://openalex.org/S4363607936","display_name":"2016 Winter Simulation Conference (WSC)","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":"2016 Winter Simulation Conference (WSC)","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/A5000889975","display_name":"Michael C. Fu","orcid":"https://orcid.org/0000-0003-2105-4932"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael C. Fu","raw_affiliation_strings":["Robert H. Smith School of Business, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert H. Smith School of Business, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5000889975"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"659","last_page":"670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9970999956130981,"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":0.9940999746322632,"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/monte-carlo-tree-search","display_name":"Monte Carlo tree search","score":0.9720152616500854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7366045713424683},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6484596133232117},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5987118482589722},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5354886651039124},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4581427276134491},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.4420754313468933},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.42569291591644287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3157271146774292},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.2122287154197693},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13835984468460083}],"concepts":[{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.9720152616500854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7366045713424683},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6484596133232117},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5987118482589722},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5354886651039124},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4581427276134491},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.4420754313468933},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42569291591644287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3157271146774292},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2122287154197693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13835984468460083},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2016.7822130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2016.7822130","pdf_url":null,"source":{"id":"https://openalex.org/S4363607936","display_name":"2016 Winter Simulation Conference (WSC)","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":"2016 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1576452626","https://openalex.org/W1601081659","https://openalex.org/W1625390266","https://openalex.org/W1714211023","https://openalex.org/W2016647253","https://openalex.org/W2100857832","https://openalex.org/W2122272911","https://openalex.org/W2168405694","https://openalex.org/W2257979135","https://openalex.org/W2505159842","https://openalex.org/W2962951833","https://openalex.org/W3011865677","https://openalex.org/W4242083795","https://openalex.org/W6636578284","https://openalex.org/W6637441126","https://openalex.org/W6765780914"],"related_works":["https://openalex.org/W3126131865","https://openalex.org/W2044344400","https://openalex.org/W2083611981","https://openalex.org/W4253186488","https://openalex.org/W1996938127","https://openalex.org/W2043380045","https://openalex.org/W4231814374","https://openalex.org/W2099557065","https://openalex.org/W2484410460","https://openalex.org/W4386874825"],"abstract_inverted_index":{"In":[0],"March":[1],"of":[2,60,111,121,143],"2016,":[3],"Google":[4],"DeepMind's":[5],"AlphaGo,":[6],"a":[7,20,51,72],"computer":[8,29],"Go-playing":[9,67],"program,":[10],"defeated":[11],"the":[12,44,83,108,125,141],"reigning":[13],"human":[14],"world":[15],"champion":[16],"Go":[17],"player,":[18],"4-1,":[19],"feat":[21],"far":[22],"more":[23],"impressive":[24],"than":[25],"previous":[26],"victories":[27],"by":[28,101,134],"programs":[30],"in":[31,66,94,128],"chess":[32],"(IBM's":[33,38],"Deep":[34],"Blue)":[35],"and":[36,149],"Jeopardy":[37],"Watson).":[39],"The":[40],"main":[41,126],"engine":[42],"behind":[43],"program":[45],"combines":[46],"machine":[47],"learning":[48],"approaches":[49],"with":[50],"technique":[52],"called":[53],"Monte":[54,61,117,130],"Carlo":[55,62,118,131],"tree":[56,63,132],"search.":[57],"Current":[58],"versions":[59],"search":[64,133],"used":[65],"algorithms":[68],"are":[69],"based":[70],"on":[71],"version":[73],"developed":[74],"for":[75,90,116],"games":[76],"that":[77],"traces":[78],"its":[79],"roots":[80],"back":[81],"to":[82,137],"adaptive":[84],"multi-stage":[85],"sampling":[86],"simulation":[87,138],"optimization":[88,139],"algorithm":[89],"estimating":[91],"value":[92],"functions":[93],"finite-horizon":[95],"Markov":[96],"decision":[97,147],"processes":[98],"(MDPs)":[99],"introduced":[100],"Chang":[102],"et":[103],"al.":[104],"(2005),":[105],"which":[106],"was":[107],"first":[109],"use":[110,142],"Upper":[112],"Confidence":[113],"Bounds":[114],"(UCBs)":[115],"simulation-based":[119],"solution":[120],"MDPs.":[122],"We":[123],"review":[124],"ideas":[127],"UCB-based":[129],"connecting":[135],"it":[136],"through":[140],"two":[144],"simple":[145],"examples:":[146],"trees":[148],"tic-tac-toe.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
