{"id":"https://openalex.org/W2155485112","doi":"https://doi.org/10.1093/comjnl/bxq006","title":"Dynamic Opponent Modelling in Fictitious Play","display_name":"Dynamic Opponent Modelling in Fictitious Play","publication_year":2010,"publication_date":"2010-02-09","ids":{"openalex":"https://openalex.org/W2155485112","doi":"https://doi.org/10.1093/comjnl/bxq006","mag":"2155485112"},"language":"en","primary_location":{"id":"doi:10.1093/comjnl/bxq006","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comjnl/bxq006","pdf_url":null,"source":{"id":"https://openalex.org/S44643521","display_name":"The Computer Journal","issn_l":"0010-4620","issn":["0010-4620","1460-2067"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Computer Journal","raw_type":"journal-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/A5033092397","display_name":"Michalis Smyrnakis","orcid":"https://orcid.org/0000-0003-1416-3727"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"M. Smyrnakis","raw_affiliation_strings":["Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040706665","display_name":"David S. Leslie","orcid":"https://orcid.org/0000-0001-5253-7676"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"D. S. Leslie","raw_affiliation_strings":["Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK","institution_ids":["https://openalex.org/I36234482"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5033092397"],"corresponding_institution_ids":["https://openalex.org/I36234482"],"apc_list":{"value":2635,"currency":"GBP","value_usd":3232},"apc_paid":null,"fwci":1.3965,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.85753576,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"53","issue":"9","first_page":"1344","last_page":"1359"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9984999895095825,"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":0.9984999895095825,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9914000034332275,"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":0.9909999966621399,"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/fictitious-play","display_name":"Fictitious play","score":0.9106612205505371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6897327303886414},{"id":"https://openalex.org/keywords/potential-game","display_name":"Potential game","score":0.5469118356704712},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5466322898864746},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.4584048092365265},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.43813008069992065},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34913909435272217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3319988250732422},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.25215432047843933},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19014695286750793},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.1426467001438141}],"concepts":[{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.9106612205505371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897327303886414},{"id":"https://openalex.org/C2778079155","wikidata":"https://www.wikidata.org/wiki/Q288500","display_name":"Potential game","level":3,"score":0.5469118356704712},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5466322898864746},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.4584048092365265},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.43813008069992065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34913909435272217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3319988250732422},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.25215432047843933},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19014695286750793},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.1426467001438141},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1093/comjnl/bxq006","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comjnl/bxq006","pdf_url":null,"source":{"id":"https://openalex.org/S44643521","display_name":"The Computer Journal","issn_l":"0010-4620","issn":["0010-4620","1460-2067"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Computer Journal","raw_type":"journal-article"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/4d8c1673-7c61-4706-91fc-05dd4a226247","is_oa":false,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/4d8c1673-7c61-4706-91fc-05dd4a226247","pdf_url":null,"source":{"id":"https://openalex.org/S7407055359","display_name":"Explore Bristol Research","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":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Smyrnakis, M & Leslie, D S 2010, 'Dynamic opponent modelling in fictitious play', The Computer Journal, vol. 53, pp. 1344-1359.","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:eprints.lancs.ac.uk:70735","is_oa":false,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/70735/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W205540638","https://openalex.org/W1548955532","https://openalex.org/W1605188341","https://openalex.org/W1632097176","https://openalex.org/W1758936897","https://openalex.org/W1980125776","https://openalex.org/W1985768685","https://openalex.org/W2066767736","https://openalex.org/W2070214254","https://openalex.org/W2096913736","https://openalex.org/W2102296778","https://openalex.org/W2104602264","https://openalex.org/W2110116921","https://openalex.org/W2119380668","https://openalex.org/W2122376145","https://openalex.org/W2126776273","https://openalex.org/W2127224665","https://openalex.org/W2130718399","https://openalex.org/W2145508886","https://openalex.org/W2149254401","https://openalex.org/W2160337655","https://openalex.org/W2237993507"],"related_works":["https://openalex.org/W3175268978","https://openalex.org/W4287266326","https://openalex.org/W3137345903","https://openalex.org/W2095833558","https://openalex.org/W4230995440","https://openalex.org/W1558762381","https://openalex.org/W4298859366","https://openalex.org/W2949401733","https://openalex.org/W1981609310","https://openalex.org/W1995227418"],"abstract_inverted_index":{"Distributed":[0],"optimization":[1,162],"can":[2,84],"be":[3],"formulated":[4],"as":[5],"an":[6,32],"n-player":[7],"coordination":[8],"game.":[9,194],"One":[10],"of":[11,48,71,99,102,109,117,123,222,230,240,270],"the":[12,87,97,107,115,118,148,177,182,186,191,203,211,214,220,228,236,265,271],"most":[13],"common":[14],"learning":[15],"techniques":[16],"in":[17,51,129,158,185,268],"game":[18,137,167],"theory":[19],"is":[20,29,208],"fictitious":[21,27,49,127,225],"play":[22,28,50,128,226],"and":[23,125,138,147,168,190,235,243],"its":[24],"variations.":[25],"However,":[26],"founded":[30],"on":[31,106,202],"implicit":[33],"assumption":[34],"that":[35],"opponents\u2019":[36,55,77],"strategies":[37,78],"are":[38],"stationary.":[39],"In":[40,213,257],"this":[41,258],"paper":[42],"we":[43,90,218,275],"present":[44],"a":[45,58,67,135,164,169,198],"new":[46],"variation":[47],"which":[52],"players":[53,146],"predict":[54],"strategy":[56,110],"using":[57],"particle":[59,223],"filter":[60,224],"algorithm.":[61],"This":[62],"allows":[63],"us":[64],"to":[65,79,95,176],"use":[66],"more":[68],"realistic":[69],"model":[70],"opponent":[72],"strategy.":[73],"We":[74,112,153],"used":[75,92],"pre-specified":[76],"examine":[80,96],"if":[81],"our":[82,103,156,260],"algorithm":[83,104,120,157,174,233,239,261,267],"efficiently":[85],"track":[86],"strategies.":[88],"Furthermore,":[89],"have":[91],"these":[93],"experiments":[94],"impact":[98],"different":[100,131,160],"values":[101],"parameters":[105],"results":[108,116,221],"tracking.":[111],"then":[113],"compared":[114,219],"proposed":[119],"with":[121,144,150,227,250],"those":[122],"stochastic":[124],"geometric":[126],"three":[130,151,272],"strategic":[132,187],"form":[133,188],"games:":[134],"potential":[136],"two":[139,145,159,269],"climbing":[140],"hill":[141],"games,":[142],"one":[143],"other":[149],"players.":[152],"also":[154],"tested":[155],"distributed":[161],"scenarios,":[163],"vehicle-target":[165,192],"assignment":[166,193],"disaster":[170,215],"management":[171,216],"problem.":[172],"Our":[173],"converges":[175],"optimum":[178],"faster":[179],"than":[180,264],"both":[181],"competitor":[183],"algorithms":[184],"games":[189],"Hence":[195],"by":[196],"placing":[197],"greater":[199],"computational":[200],"demand":[201],"individual":[204],"agents,":[205],"less":[206],"communication":[207],"required":[209],"between":[210],"agents.":[212],"scenario":[217,259],"ones":[229],"Matlab's":[231],"centralized":[232,237],"bintprog":[234],"pre-planning":[238,266],"(Gelenbe,":[241],"E.":[242],"Timotheou,":[244],"S.":[245],"(2008)":[246],"Random":[247],"neural":[248],"networks":[249],"synchronized":[251],"interactions.":[252],"Neural":[253],"Comput.,":[254],"20(9),":[255],"2308\u20132324).":[256],"performed":[262],"better":[263],"performance":[273],"measures":[274],"used.":[276]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
