{"id":"https://openalex.org/W4308390426","doi":"https://doi.org/10.1109/allerton49937.2022.9929423","title":"Decentralized Anomaly Detection via Deep Multi-Agent Reinforcement Learning","display_name":"Decentralized Anomaly Detection via Deep Multi-Agent Reinforcement Learning","publication_year":2022,"publication_date":"2022-09-27","ids":{"openalex":"https://openalex.org/W4308390426","doi":"https://doi.org/10.1109/allerton49937.2022.9929423"},"language":"en","primary_location":{"id":"doi:10.1109/allerton49937.2022.9929423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","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/A5016627257","display_name":"Hadar Szostak","orcid":null},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Hadar Szostak","raw_affiliation_strings":["School of Electrical and Computer Engineering, Ben-Gurion University of the Negev,Beer-Sheva,Israel,84105"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Ben-Gurion University of the Negev,Beer-Sheva,Israel,84105","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066085947","display_name":"Kobi Cohen","orcid":"https://orcid.org/0000-0003-0532-009X"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Kobi Cohen","raw_affiliation_strings":["School of Electrical and Computer Engineering, Ben-Gurion University of the Negev,Beer-Sheva,Israel,84105"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Ben-Gurion University of the Negev,Beer-Sheva,Israel,84105","institution_ids":["https://openalex.org/I124227911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016627257"],"corresponding_institution_ids":["https://openalex.org/I124227911"],"apc_list":null,"apc_paid":null,"fwci":1.061,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80928998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9983999729156494,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9983999729156494,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.695712685585022},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6941574811935425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6387525200843811},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5510938763618469},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5353838205337524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.524158239364624},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5161753296852112},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4581564664840698},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4261147379875183},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40966492891311646},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.3365548253059387},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33623528480529785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32327160239219666},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.302844762802124},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18212801218032837}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.695712685585022},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6941574811935425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6387525200843811},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5510938763618469},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5353838205337524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.524158239364624},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5161753296852112},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4581564664840698},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4261147379875183},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40966492891311646},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3365548253059387},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33623528480529785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32327160239219666},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.302844762802124},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18212801218032837},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton49937.2022.9929423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5755329095","display_name":null,"funder_award_id":"2640/20","funder_id":"https://openalex.org/F4320322252","funder_display_name":"Israel Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1978470213","https://openalex.org/W1990038735","https://openalex.org/W1990503235","https://openalex.org/W2019645207","https://openalex.org/W2045030310","https://openalex.org/W2070495730","https://openalex.org/W2077397896","https://openalex.org/W2082167858","https://openalex.org/W2101643234","https://openalex.org/W2153966789","https://openalex.org/W2166205892","https://openalex.org/W2498422553","https://openalex.org/W2512254099","https://openalex.org/W2736601468","https://openalex.org/W2743681928","https://openalex.org/W2785315072","https://openalex.org/W2896121142","https://openalex.org/W2903142609","https://openalex.org/W2912108923","https://openalex.org/W2945910721","https://openalex.org/W2963658727","https://openalex.org/W2963912378","https://openalex.org/W2974976221","https://openalex.org/W2986276296","https://openalex.org/W2999204576","https://openalex.org/W3009317244","https://openalex.org/W3012943290","https://openalex.org/W3046947688","https://openalex.org/W3099581246","https://openalex.org/W3110383331","https://openalex.org/W3127570909","https://openalex.org/W3148526481","https://openalex.org/W3187243030","https://openalex.org/W3195411792","https://openalex.org/W4232896127","https://openalex.org/W4283821341","https://openalex.org/W4285175639","https://openalex.org/W4299802797","https://openalex.org/W4306179383","https://openalex.org/W4386839763","https://openalex.org/W6738796088","https://openalex.org/W6741002519","https://openalex.org/W6747941106","https://openalex.org/W6838811763","https://openalex.org/W6846392434","https://openalex.org/W6855986415"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"We":[0,105],"consider":[1],"a":[2,13,18,29,70,102,107],"decentralized":[3],"anomaly":[4,99],"detection":[5,100],"problem,":[6,137],"where":[7],"multiple":[8],"agents":[9,64],"collaborate":[10],"to":[11,117,130],"localize":[12],"single":[14,103],"anomalous":[15],"process":[16,46,55],"among":[17,89],"finite":[19],"number":[20],"<tex":[21],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[22],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$M$</tex>":[23],"of":[24,31,80,127],"processes.":[25],"At":[26],"each":[27,38,44],"time,":[28],"subset":[30],"the":[32,41,54,76,81,85,90,119,125,128,140,145],"processes":[33],"can":[34],"be":[35],"observed":[36],"by":[37,101,143],"agent,":[39],"and":[40,84,138],"observations":[42],"from":[43],"chosen":[45],"follow":[47],"two":[48],"different":[49],"distributions,":[50],"depending":[51],"on":[52,111],"whether":[53],"is":[56,65,69],"normal":[57],"or":[58],"abnormal.":[59],"The":[60,67],"communication":[61],"channel":[62],"between":[63],"rate-limited.":[66],"objective":[68],"sequential":[71],"search":[72],"strategy":[73],"that":[74,97],"minimizes":[75],"Bayes":[77,120],"risk,":[78],"consisting":[79],"sampling":[82],"cost,":[83],"joint":[86],"terminal":[87],"cost":[88],"agents.":[91],"This":[92],"problem":[93],"generalizes":[94],"previous":[95],"studies":[96],"considered":[98],"detector.":[104],"develop":[106],"novel":[108],"algorithm":[109,129],"based":[110],"deep":[112],"multi-agent":[113,147],"reinforcement":[114],"learning":[115,149],"optimization":[116],"minimize":[118],"risk.":[121],"Numerical":[122],"experiments":[123],"demonstrate":[124],"ability":[126],"learn":[131],"good":[132],"policies":[133],"in":[134],"this":[135],"challenging":[136],"improve":[139],"single-agent":[141],"performance":[142],"applying":[144],"proposed":[146],"collaborative":[148],"method.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
