{"id":"https://openalex.org/W3213681221","doi":"https://doi.org/10.1145/3474717.3483924","title":"Learning Decision Making Strategies of Non-experts","display_name":"Learning Decision Making Strategies of Non-experts","publication_year":2021,"publication_date":"2021-11-02","ids":{"openalex":"https://openalex.org/W3213681221","doi":"https://doi.org/10.1145/3474717.3483924","mag":"3213681221"},"language":"en","primary_location":{"id":"doi:10.1145/3474717.3483924","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474717.3483924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","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/A5027949894","display_name":"Menghai Pan","orcid":"https://orcid.org/0000-0002-8390-7147"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Menghai Pan","raw_affiliation_strings":["Worcester Polytechnic Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327554","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0003-0289-1452"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["Worcester Polytechnic Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100630059","display_name":"Yanhua Li","orcid":"https://orcid.org/0000-0001-8972-503X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanhua Li","raw_affiliation_strings":["Worcester Polytechnic Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198510","display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["University of Iowa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Iowa","institution_ids":["https://openalex.org/I126307644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106731907","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-2032-0381"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Lenovo Group Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Group Limited","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16340208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"149","last_page":"158"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7400804162025452},{"id":"https://openalex.org/keywords/trips-architecture","display_name":"TRIPS architecture","score":0.6125756502151489},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.6088191270828247},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5644328594207764},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5069894790649414},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48294156789779663},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4822190999984741},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.45934587717056274},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4357936680316925},{"id":"https://openalex.org/keywords/urban-computing","display_name":"Urban computing","score":0.4291284382343292},{"id":"https://openalex.org/keywords/human-behavior","display_name":"Human behavior","score":0.4181898236274719}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400804162025452},{"id":"https://openalex.org/C157085824","wikidata":"https://www.wikidata.org/wiki/Q2384809","display_name":"TRIPS architecture","level":2,"score":0.6125756502151489},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.6088191270828247},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5644328594207764},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5069894790649414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48294156789779663},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4822190999984741},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.45934587717056274},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4357936680316925},{"id":"https://openalex.org/C2778459138","wikidata":"https://www.wikidata.org/wiki/Q7900107","display_name":"Urban computing","level":2,"score":0.4291284382343292},{"id":"https://openalex.org/C117035363","wikidata":"https://www.wikidata.org/wiki/Q3769299","display_name":"Human behavior","level":2,"score":0.4181898236274719},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474717.3483924","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474717.3483924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G8389364601","display_name":null,"funder_award_id":"IIS-1942680, CNS-1952085, CMMI-1831140, and DGE-2021871","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W64088143","https://openalex.org/W115285041","https://openalex.org/W1515851193","https://openalex.org/W1585398001","https://openalex.org/W1691728462","https://openalex.org/W1965555277","https://openalex.org/W1976993400","https://openalex.org/W1982391657","https://openalex.org/W1999874108","https://openalex.org/W2006154742","https://openalex.org/W2044414703","https://openalex.org/W2061562262","https://openalex.org/W2078115429","https://openalex.org/W2098774185","https://openalex.org/W2099471712","https://openalex.org/W2112738128","https://openalex.org/W2132968912","https://openalex.org/W2133068870","https://openalex.org/W2138198492","https://openalex.org/W2150712216","https://openalex.org/W2161581167","https://openalex.org/W2295718484","https://openalex.org/W2434014514","https://openalex.org/W2434741482","https://openalex.org/W2489128515","https://openalex.org/W2538506242","https://openalex.org/W2617561348","https://openalex.org/W2621350877","https://openalex.org/W2737047298","https://openalex.org/W2770884134","https://openalex.org/W2794679218","https://openalex.org/W2897791609","https://openalex.org/W2936204277","https://openalex.org/W2944211469","https://openalex.org/W2949416428","https://openalex.org/W2949608212","https://openalex.org/W2963508354","https://openalex.org/W2963830550","https://openalex.org/W2963896050","https://openalex.org/W2964337551","https://openalex.org/W3003540860","https://openalex.org/W3013077068","https://openalex.org/W3081056513","https://openalex.org/W3126748244"],"related_works":["https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W3090765184","https://openalex.org/W2989156809","https://openalex.org/W1063558733"],"abstract_inverted_index":{"Thanks":[0],"to":[1,49,74,125,155,163,176,197],"the":[2,17,64,127,145,167,179,189,199,218,221],"rapid":[3],"development":[4],"of":[5,67,123,201,220],"mobile":[6],"sensing":[7],"techniques,":[8],"massive":[9],"human-generated":[10],"spatial-temporal":[11,164],"data":[12,89,193],"(HSTD)":[13],"are":[14],"generated":[15],"from":[16,23,30,45,57,78,91,166],"urban":[18,31],"areas,":[19],"e.g.,":[20],"passenger-seeking":[21,55],"trajectories":[22],"taxi":[24,59,191],"drivers,":[25],"and":[26,114],"public":[27],"transit":[28],"trips":[29],"dwellers.":[32],"These":[33],"HSTD":[34,46,79,98],"record":[35],"sequential":[36],"decisions":[37],"made":[38],"by":[39],"human":[40,43,76,94],"agents.":[41],"Studying":[42],"behavior":[44,77],"provides":[47],"benefits":[48],"many":[50],"aspects,":[51],"for":[52,130,151,225],"example,":[53],"studying":[54],"strategies":[56],"experienced":[58,119],"drivers":[60],"can":[61,173],"help":[62],"improve":[63],"operation":[65],"efficiencies":[66],"those":[68],"new":[69],"drivers.":[70],"One":[71],"common":[72],"method":[73],"analyze":[75],"is":[80,161],"Imitation":[81,149],"Learning":[82,150],"(IL).":[83],"Existing":[84],"IL":[85],"approaches":[86,216],"rely":[87],"on":[88],"collected":[90],"experts.":[92],"However,":[93],"agents":[95,131],"who":[96],"generate":[97],"may":[99],"have":[100],"diverse":[101],"expertise":[102],"levels":[103],"across":[104],"geographical":[105],"regions,":[106,165,177],"i.e.,":[107],"with":[108],"good":[109],"policies":[110,116],"in":[111,117,132],"some":[112],"regions":[113,137],"poor":[115],"less":[118],"regions.":[120],"The":[121,205],"problem":[122],"how":[124],"infer":[126],"optimal":[128,223],"policy":[129,224],"their":[133],"unfamiliar":[134],"or":[135],"less-experienced":[136],"remains":[138],"open.":[139],"In":[140],"this":[141],"paper,":[142],"we":[143],"propose":[144],"novel":[146],"Generative":[147],"Adversarial":[148],"Non-experts":[152],"(NEXT-GAIL)":[153],"framework":[154],"first":[156],"disentangle":[157],"expert":[158,185],"knowledge,":[159],"which":[160],"irrelevant":[162],"demonstration":[168],"data.":[169],"Then,":[170],"such":[171],"knowledge":[172],"be":[174],"transferred":[175],"where":[178],"agent":[180],"does":[181],"not":[182],"possess":[183],"an":[184,195],"policy.":[186],"We":[187],"take":[188],"real-world":[190],"trajectory":[192],"as":[194],"example":[196],"evaluate":[198],"performance":[200],"our":[202,210],"proposed":[203,211],"framework.":[204],"comparison":[206],"results":[207],"illustrate":[208],"that":[209],"NEXT-GAIL":[212],"outperforms":[213],"existing":[214],"state-of-the-art":[215],"regarding":[217],"accuracy":[219],"inferred":[222],"non-experts.":[226]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}