{"id":"https://openalex.org/W4403577495","doi":"https://doi.org/10.1145/3627673.3679714","title":"Discovering Denial Constraints Based on Deep Reinforcement Learning","display_name":"Discovering Denial Constraints Based on Deep Reinforcement Learning","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577495","doi":"https://doi.org/10.1145/3627673.3679714"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679714","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679714","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 Information and Knowledge Management","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/A5106403590","display_name":"Lingfeng Bian","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfeng Bian","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0007-2023-142X","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101865874","display_name":"Weidong Yang","orcid":"https://orcid.org/0000-0002-6473-9272"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210110560","display_name":"Zhuhai Fudan Innovation Research Institute","ror":"https://ror.org/01tapk317","country_code":"CN","type":"facility","lineage":["https://openalex.org/I24943067","https://openalex.org/I4210110560"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Yang","raw_affiliation_strings":["Fudan University &amp; Zhuhai Fudan Innovation Research Institute, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6473-9272","affiliations":[{"raw_affiliation_string":"Fudan University &amp; Zhuhai Fudan Innovation Research Institute, Shanghai, China","institution_ids":["https://openalex.org/I4210110560","https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102592809","display_name":"Jingyi Xu","orcid":"https://orcid.org/0009-0003-6474-2695"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Xu","raw_affiliation_strings":["Fudan University &amp; AVIC United Technology Center for Basic Research on Aircraft System Fault Diagnosis and Health Management, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0003-6474-2695","affiliations":[{"raw_affiliation_string":"Fudan University &amp; AVIC United Technology Center for Basic Research on Aircraft System Fault Diagnosis and Health Management, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005944481","display_name":"Zijing Tan","orcid":"https://orcid.org/0000-0001-6332-780X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijing Tan","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6332-780X","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65780502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"120","last_page":"129"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9851999878883362,"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":0.9851999878883362,"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/T10260","display_name":"Software Engineering Research","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.972000002861023,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8165369629859924},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7092864513397217},{"id":"https://openalex.org/keywords/denial","display_name":"Denial","score":0.592564582824707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4784386456012726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32023561000823975},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.136476069688797},{"id":"https://openalex.org/keywords/psychotherapist","display_name":"Psychotherapist","score":0.058063805103302}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8165369629859924},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092864513397217},{"id":"https://openalex.org/C2780900520","wikidata":"https://www.wikidata.org/wiki/Q100268981","display_name":"Denial","level":2,"score":0.592564582824707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4784386456012726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32023561000823975},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.136476069688797},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.058063805103302}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679714","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679714","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 Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1976732638","https://openalex.org/W1980185632","https://openalex.org/W2044469685","https://openalex.org/W2046298800","https://openalex.org/W2113607096","https://openalex.org/W2152228468","https://openalex.org/W2153531471","https://openalex.org/W2170712852","https://openalex.org/W2190899134","https://openalex.org/W2421610675","https://openalex.org/W2439326083","https://openalex.org/W2591700809","https://openalex.org/W2766447205","https://openalex.org/W2772675153","https://openalex.org/W2983641625","https://openalex.org/W2997756720","https://openalex.org/W3005822199","https://openalex.org/W3015738839","https://openalex.org/W3030496122","https://openalex.org/W3046745582","https://openalex.org/W3085990079","https://openalex.org/W3098444442","https://openalex.org/W4312628841","https://openalex.org/W4321448321","https://openalex.org/W4380433154","https://openalex.org/W4381621947","https://openalex.org/W4385270181"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Numerous":[0],"algorithms":[1],"have":[2],"been":[3],"proposed":[4],"for":[5,15,51],"discovering":[6,25,101],"denial":[7],"constraints":[8],"(DCs),":[9],"which":[10,65],"are":[11,49],"essential":[12],"and":[13,55,74,123,140,151,163,173],"effective":[14],"maintaining":[16],"data":[17,121],"consistency.":[18],"However,":[19],"existing":[20],"methods":[21,59],"only":[22],"focus":[23],"on":[24,160],"the":[26,61,67,100,120,125,145],"complete":[27],"set":[28],"of":[29,38,40,47,63,127],"DCs,":[30],"often":[31],"resulting":[32],"in":[33],"hundreds":[34],"or":[35],"even":[36],"tens":[37],"thousands":[39],"discovered":[41,68,146],"rules.":[42,175],"Such":[43],"a":[44,84,104,113,132],"large":[45],"number":[46],"DCs":[48,69],"impractical":[50],"users":[52],"to":[53,70,109,118,143],"verify":[54],"utilize.":[56],"Besides,":[57],"these":[58,79],"overlook":[60],"intent":[62],"users,":[64],"requires":[66],"be":[71],"succinct,":[72,171],"relevant,":[73,172],"diverse":[75,174],"concurrently.":[76],"To":[77],"address":[78],"limitations,":[80],"we":[81,97,130,152],"introduce":[82],"DCMiner,":[83],"deep":[85],"reinforcement":[86],"learning":[87],"(DRL)-based":[88],"framework":[89],"that":[90,135,167],"produces":[91],"rules":[92,147],"satisfying":[93],"user":[94,149],"preferences.":[95],"Specifically,":[96],"first":[98],"model":[99,115],"process":[102,108],"via":[103],"kCover":[105],"Markov":[106],"decision":[107],"improve":[110],"efficiency.":[111],"Then,":[112],"graphQ":[114],"is":[116],"introduced":[117],"capture":[119],"distribution":[122],"facilitate":[124],"discovery":[126],"DCs.":[128],"Lastly,":[129],"design":[131],"reward":[133],"function":[134],"flexibly":[136],"integrates":[137],"both":[138,161],"objective":[139],"subjective":[141],"criteria":[142],"align":[144],"with":[148],"intent,":[150],"propose":[153],"an":[154],"efficient":[155],"training":[156],"process.":[157],"Extensive":[158],"experiments":[159],"real-world":[162],"synthetic":[164],"datasets":[165],"show":[166],"DCMiner":[168],"can":[169],"discover":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
