{"id":"https://openalex.org/W7114905605","doi":"https://doi.org/10.1145/3748636.3762719","title":"ConPro-GAIL: Interpretable Policy Learning via Conceptual Prototyping for Human Spatiotemporal Decision Understanding","display_name":"ConPro-GAIL: Interpretable Policy Learning via Conceptual Prototyping for Human Spatiotemporal Decision Understanding","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W7114905605","doi":"https://doi.org/10.1145/3748636.3762719"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3762719","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748636.3762719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM 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":null,"display_name":"Ronilo Ragodos","orcid":"https://orcid.org/0000-0002-8832-0994"},"institutions":[{"id":"https://openalex.org/I161057412","display_name":"University of New Hampshire","ror":"https://ror.org/01rmh9n78","country_code":"US","type":"education","lineage":["https://openalex.org/I161057412"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ronilo Ragodos","raw_affiliation_strings":["University of New Hampshire, Durham, New Hampshire, USA"],"affiliations":[{"raw_affiliation_string":"University of New Hampshire, Durham, New Hampshire, USA","institution_ids":["https://openalex.org/I161057412"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xun Zhou","orcid":"https://orcid.org/0000-0003-4930-6572"},"institutions":[{"id":"https://openalex.org/I158809036","display_name":"Shenzhen Institute of Information Technology","ror":"https://ror.org/03wrf9427","country_code":"CN","type":"education","lineage":["https://openalex.org/I158809036"]},{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xun Zhou","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, China","Pengcheng Laboratory, Shenzhen, China","Shenzhen Loop Area Institute, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Shenzhen Loop Area Institute, Shenzhen, China","institution_ids":["https://openalex.org/I158809036"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tong Wang","orcid":"https://orcid.org/0000-0001-8687-4208"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Wang","raw_affiliation_strings":["Yale University, Connecticut, USA"],"affiliations":[{"raw_affiliation_string":"Yale University, Connecticut, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yajun Pan","orcid":"https://orcid.org/0009-0003-6511-9299"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajun Pan","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-2032-0381"},"institutions":[{"id":"https://openalex.org/I4210131801","display_name":"Hong Kong R&D Centre for Logistics and Supply Chain Management Enabling Technologies","ror":"https://ror.org/03nm59d75","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210131801"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Research &amp; Technology Development, Logistics and Supply Chain MultiTech R&amp;D Centre, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Research &amp; Technology Development, Logistics and Supply Chain MultiTech R&amp;D Centre, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I4210131801"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161057412"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6082002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.24330000579357147,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.24330000579357147,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.20649999380111694,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.19629999995231628,"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/interpretability","display_name":"Interpretability","score":0.9361000061035156},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6996999979019165},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5738999843597412},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.49219998717308044},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4884999990463257},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.41100001335144043},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3919999897480011},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.391400009393692},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.3756999969482422}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9361000061035156},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6996999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6990000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801999807357788},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5845999717712402},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5738999843597412},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2984634286","wikidata":"https://www.wikidata.org/wiki/Q1331926","display_name":"Decision process","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C117035363","wikidata":"https://www.wikidata.org/wiki/Q3769299","display_name":"Human behavior","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C115988155","wikidata":"https://www.wikidata.org/wiki/Q3262192","display_name":"Decision problem","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C186116695","wikidata":"https://www.wikidata.org/wiki/Q5249226","display_name":"Decision analysis","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C59594135","wikidata":"https://www.wikidata.org/wiki/Q5249242","display_name":"Decision model","level":2,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3762719","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748636.3762719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7484826445579529,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G3847681606","display_name":null,"funder_award_id":"2025A1515011258","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G5661046512","display_name":null,"funder_award_id":"62472125","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2944211469","https://openalex.org/W2964256412","https://openalex.org/W3035602609","https://openalex.org/W3038629022","https://openalex.org/W3081056513","https://openalex.org/W3109631476","https://openalex.org/W3126748244","https://openalex.org/W3137737764","https://openalex.org/W3167738759","https://openalex.org/W3172917901","https://openalex.org/W3188824417","https://openalex.org/W3215928733","https://openalex.org/W4308613785","https://openalex.org/W4365394967","https://openalex.org/W4386071596","https://openalex.org/W4409365371"],"related_works":[],"abstract_inverted_index":{"The":[0],"problem":[1,71],"of":[2,10,141,144],"human":[3,18,89],"spatiotemporal":[4],"(ST)":[5],"decision":[6,15,157],"understanding,":[7],"which":[8,56],"consists":[9],"extracting":[11],"faithful":[12],"and":[13,24,38,78,104,134,181,200],"interpretable":[14,50,123,128],"strategies":[16],"from":[17,166],"agents'":[19],"behavioral":[20],"records":[21],"in":[22,60,107,139,151,169],"space":[23],"time,":[25],"is":[26,41],"important":[27],"for":[28,91,126],"many":[29],"applications,":[30],"such":[31,95],"as":[32,48,51,72],"improving":[33],"taxi":[34,186],"drivers'":[35],"route":[36],"planning":[37],"efficiency.":[39],"It":[40,154],"challenging":[42],"because":[43],"ST":[44,112,129],"data":[45],"are":[46],"not":[47],"readily":[49],"images":[52],"or":[53],"text":[54],"data,":[55],"leads":[57],"to":[58,82,148,175],"difficulties":[59],"constructing":[61],"data-driven":[62],"explanations.":[63],"Existing":[64],"research":[65],"on":[66,184],"this":[67],"topic":[68],"defines":[69],"the":[70,87,136,152,170,176],"a":[73,84,121,156],"Markov":[74],"Decision":[75],"Process":[76],"(MDP)":[77],"uses":[79],"imitation":[80],"learning":[81],"extract":[83],"policy":[85,90,130,138,194],"approximating":[86],"underlying":[88],"post-hoc":[92,204],"interpretation.":[93],"However,":[94],"methods":[96],"cannot":[97],"provide":[98],"direct":[99],"interpretation":[100],"through":[101],"model":[102,125],"training":[103],"may":[105],"result":[106],"incomprehensible":[108],"interpretations":[109],"when":[110],"using":[111],"data.":[113],"We":[114],"address":[115],"these":[116],"limitations":[117],"by":[118],"designing":[119],"ConPro-GAIL,":[120],"prototype-based":[122],"GAIL":[124],"intrinsically":[127],"extraction.":[131],"ConPro-GAIL":[132,191],"learns":[133],"represents":[135],"optimal":[137],"terms":[140],"prototypical":[142],"sets":[143],"concepts":[145],"that":[146,190],"correspond":[147],"general":[149],"scenarios":[150],"MDP.":[153],"explains":[155],"associated":[158],"with":[159],"an":[160],"input":[161,177],"state":[162,178],"via":[163],"inductive":[164],"generalization":[165],"what":[167],"occurred":[168],"state's":[171],"most":[172],"similar":[173],"prototypes":[174],"itself.":[179],"Experiments":[180],"case":[182],"studies":[183],"two":[185],"trajectory":[187],"datasets":[188],"show":[189],"achieves":[192],"better":[193,201],"faithfulness":[195],"than":[196,203],"its":[197],"black-box":[198],"competitors":[199],"interpretability":[202],"explainers.":[205]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-12T00:00:00"}
