{"id":"https://openalex.org/W2250891614","doi":"https://doi.org/10.18653/v1/w15-4643","title":"Optimising Turn-Taking Strategies With Reinforcement Learning","display_name":"Optimising Turn-Taking Strategies With Reinforcement Learning","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2250891614","doi":"https://doi.org/10.18653/v1/w15-4643","mag":"2250891614"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w15-4643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-4643","pdf_url":"https://www.aclweb.org/anthology/W15-4643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W15-4643.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084693979","display_name":"Hatim Khouzaimi","orcid":null},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hatim KHOUZAIMI","raw_affiliation_strings":["Orange Labs LIA-CERI France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orange Labs LIA-CERI France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089214987","display_name":"Romain Laroche","orcid":"https://orcid.org/0000-0001-7180-2746"},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Romain Laroche","raw_affiliation_strings":["Orange Labs, Issy-les-Moulineaux, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orange Labs, Issy-les-Moulineaux, France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103414469","display_name":"Fabrice Lef\u00e8vre","orcid":null},"institutions":[{"id":"https://openalex.org/I198415970","display_name":"Universit\u00e9 d'Avignon et des Pays de Vaucluse","ror":"https://ror.org/00mfpxb84","country_code":"FR","type":"education","lineage":["https://openalex.org/I198415970"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fabrice Lefevre","raw_affiliation_strings":["LIA-CERI, Univ. Avignon, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIA-CERI, Univ. Avignon, France","institution_ids":["https://openalex.org/I198415970"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"315","last_page":"324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":1.0,"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/T12031","display_name":"Speech and dialogue systems","score":1.0,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.9973999857902527,"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/T10028","display_name":"Topic Modeling","score":0.9921000003814697,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.9167464971542358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7732467651367188},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6642100811004639},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5812910199165344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5082897543907166},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47950154542922974},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4422295093536377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16119199991226196},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07880496978759766}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9167464971542358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732467651367188},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6642100811004639},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5812910199165344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5082897543907166},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47950154542922974},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4422295093536377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16119199991226196},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07880496978759766},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w15-4643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-4643","pdf_url":"https://www.aclweb.org/anthology/W15-4643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w15-4643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-4643","pdf_url":"https://www.aclweb.org/anthology/W15-4643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2250891614.pdf","grobid_xml":"https://content.openalex.org/works/W2250891614.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W37080510","https://openalex.org/W121610373","https://openalex.org/W200223693","https://openalex.org/W1605030600","https://openalex.org/W1691659217","https://openalex.org/W1757949796","https://openalex.org/W1975244201","https://openalex.org/W1998677696","https://openalex.org/W2026431544","https://openalex.org/W2054716580","https://openalex.org/W2067348796","https://openalex.org/W2086645471","https://openalex.org/W2095271936","https://openalex.org/W2104300420","https://openalex.org/W2106007254","https://openalex.org/W2106547558","https://openalex.org/W2109038907","https://openalex.org/W2110633879","https://openalex.org/W2121863487","https://openalex.org/W2150609911","https://openalex.org/W2150775217","https://openalex.org/W2153190547","https://openalex.org/W2250736710","https://openalex.org/W2251062710","https://openalex.org/W2278865652","https://openalex.org/W2289676958","https://openalex.org/W2312609093","https://openalex.org/W2399301052","https://openalex.org/W2586680856","https://openalex.org/W3011120880","https://openalex.org/W4214717370","https://openalex.org/W4232432298","https://openalex.org/W4246860817","https://openalex.org/W4248634141","https://openalex.org/W4300554125","https://openalex.org/W4323322863"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W2031695474","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2586732548","https://openalex.org/W3049728571"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"reinforcement":[3],"learning":[4,81],"(RL)":[5],"is":[6,52,78],"used":[7,79],"to":[8,84,107],"learn":[9],"an":[10,92,100],"efficient":[11],"turn-taking":[12],"management":[13],"model":[14],"in":[15,38,113],"a":[16,39,49,60,74,89],"simulated":[17],"slotfilling":[18],"task":[19,32],"with":[20,73],"the":[21,25,30,43,57,82,105],"objective":[22],"of":[23,65],"minimising":[24],"dialogue":[26,47,50],"duration":[27],"and":[28,56,91,99],"maximising":[29],"completion":[31],"ratio.":[33],"Turn-taking":[34],"decisions":[35],"are":[36],"handled":[37],"separate":[40],"new":[41],"module,":[42],"Scheduler.":[44],"Unlike":[45],"most":[46],"systems,":[48],"turn":[51],"split":[53],"into":[54],"microturns":[55],"Scheduler":[58],"makes":[59],"decision":[61],"for":[62,80],"each":[63],"one":[64],"them.":[66],"A":[67],"Fitted":[68],"Value":[69],"Iteration":[70],"algorithm,":[71],"Fitted-Q,":[72],"linear":[75],"state":[76,83],"representation":[77],"action":[85],"policy.":[86],"Comparison":[87],"between":[88],"non-incremental":[90],"incremental":[93,101],"handcrafted":[94],"strategies,":[95],"taken":[96],"as":[97],"baselines,":[98],"RL-based":[102],"strategy,":[103],"shows":[104],"latter":[106],"be":[108],"significantly":[109],"more":[110],"efficient,":[111],"especially":[112],"noisy":[114],"environments.":[115]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}