{"instituterec":{"StatusID":1,"InsID":13971,"StandardName":"Artificial Intelligence Laboratory","OrigName":"Artificial Intelligence Laboratory","OrigNameLangCode":"en","OrigNameLangID":15,"Acronym":"VUB","HigherInsID":13972,"VlizCoreFlag":1,"AdrID":null,"Line1":null,"Line2":null,"Line3":null,"Line4":null,"Phone":null,"GSM":null,"Email":null,"Lat":null,"Lon":null,"OrigNameLang":"English","OrigNameLangNL":"Engels","AbstractEnglish":"The Artificial Intelligence laboratory has expertise in computational creativity, emergent communication and language, computational construction grammar, reinforcement learning, game theory, preference handling, computational biology, computer vision and deep learning, learning in multi-agent systems, and machine learning for data mining. They focus on developing computational systems that exhibit creative behaviors, self-organising languages, mapping natural language to meaning representations, and self-learning systems. They also analyse human decision-making strategies, study social dilemmas and participant decision-making, apply data-analytical and simulation techniques to biological systems, and explore computer vision and deep learning in various domains. Their research extends to decentralised systems, cognitive AI, and the evolution of speech using AI methods.\r\n\r\nIn the marine field, the group explores the following topics:\r\n<ul><li>vocal plasticity in harbor seal pups, investigating the potential for changes in their vocalisations over time;</li><li>developing a framework for automatically and continuously classifying Supervisory Control and Data Acquisition (SCADA) data from an offshore wind farm into different design load cases. By analysing the data, the effects of wake on the loading conditions experienced by wind turbines is assessed. This data-driven approach using sensor-equipped turbines allows for a more detailed understanding of the loading events throughout the turbines' lifetime, which can inform future design iterations for improved optimisation based on realistic conditions.</li></ul>","AbstractOtherLang":"De onderzoeksgroep Artificial Intellegence heeft expertise in computationele creativiteit, emergente communicatie en taal, computationele constructiegrammatica, reinforcement learning, speltheorie, preferentieverwerking, computationele biologie, computervisie en deep learning, leren in multi-agent systemen en machinaal leren voor datamining. De groep richt zich op de ontwikkeling van computationele systemen die creatief gedrag vertonen, zelforganiserende talen, het in kaart brengen van natuurlijke taal naar betekenisrepresentaties, en zelflerende systemen. Daarnaast analyseert de onderzoeksgroep ook menselijke besluitvormingsstrategieën, bestudeert ze sociale dilemma's en besluitvorming door deelnemers, past ze data-analytische en simulatietechnieken toe op biologische systemen en verkent ze computervisie en deep learning in verschillende domeinen. Hun onderzoek strekt zich uit tot gedecentraliseerde systemen, cognitieve AI en de evolutie van spraak met behulp van AI-methoden.\r\n\r\nOp marien gebied onderzoekt de groep de volgende onderwerpen:\r\n<ul><li>vocale plasticiteit bij zeehondenpups, waarbij het potentieel voor veranderingen in hun vocalisaties in de tijd wordt onderzocht;</li><li>ontwikkeling van een kader voor het automatisch en continu classificeren van SCADA-gegevens (Supervisory Control and Data Acquisition) van een offshore-windmolenpark in verschillende ontwerpbelastingsgevallen. Door de gegevens te analyseren worden de effecten van de wervelstroom op de door de windturbines ervaren belasting beoordeeld. Deze datagestuurde aanpak, gebruikmakend van met sensoren uitgeruste turbines, resulteert in een gedetailleerder begrip  van de belastingsgebeurtenissen gedurende de levensduur van de turbines, hetgeen informatie kan opleveren voor toekomstige ontwerpiteraties voor een betere optimalisatie op basis van realistische omstandigheden.</li></ul>","AbstractLangCode":null,"AbstractLangID":null,"AbstractLang":null,"AbstractLangNL":null,"SuccessorOfInsID":null,"DateLastModified":{"date":"2024-06-04 01:34:19.073000","timezone_type":1,"timezone":"+00:00"},"PrevIns":null,"PrevAcro":null,"PublicFlag":1,"CheckedFlag":0,"ParID":2956,"InstituteType":"Scientific","EnvName":null,"ISO3166":null,"LevelName":null,"ND":"2017-06-13","UD":"2021-10-29","EncAddress":""},"parent":{"PublicFlag":1,"InsID":13972,"OrigNameLangCode":"en","OrigNameLangID":15,"FullStandardName":"Vrije Universiteit Brussel; Vakgroep Computerwetenschappen","FullOrigName":"Vrije Universiteit Brussel; Faculty of Science and Bio-engineering Sciences; Department of Computer Science","Acronym":"VUB"},"institutes":null,"references":[{"BRefID":391417,"RR":"<b>Kocsis, K.; Duengen, D.; Jadoul, Y.; Ravignani, A.</b> (2024). Harbour seals use rhythmic percussive signalling in interaction and display. <i>Anim. Behav. 207</i>: 223-234. <a href=\"https://dx.doi.org/10.1016/j.anbehav.2023.09.014\" target=\"_blank\">https://dx.doi.org/10.1016/j.anbehav.2023.09.014</a>","PeerRev":1},{"BRefID":391512,"RR":"<b>Anichini, M.; de Reus, K.; Hersh, T.A.; Valente, D.; Salazar-Casals, A.; Berry, C.; Keller, P.E.; Ravignani, A.</b> (2023). Measuring rhythms of vocal interactions: a proof of principle in harbour seal pups. <i>Phil. Trans. R. Soc. Lond. (B Biol. Sci.) 378(1875)</i>: 20210477. <a href=\"https://dx.doi.org/10.1098/rstb.2021.0477\" target=\"_blank\">https://dx.doi.org/10.1098/rstb.2021.0477</a>","PeerRev":1},{"BRefID":382919,"RR":"<b>Chesterman, X.; Verstraeten, T.; Daems, P.J.; Nowé, A.; Helsen, J.</b> (2023). Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms. <i>Wind Energy Science 8(6)</i>: 893-924. <a href=\"https://dx.doi.org/10.5194/wes-8-893-2023\" target=\"_blank\">https://dx.doi.org/10.5194/wes-8-893-2023</a>","PeerRev":1},{"BRefID":382998,"RR":"<b>Salazar-Casals, A.; de Reus, K.; Greskewitz, N.; Havermans, J.; Geut, M.; Villanueva, S.; Rubio-Garcia, A.</b> (2022). Increased incidence of entanglements and ingested marine debris in Dutch seals from 2010 to 2020. <i>Oceans 3(3)</i>: 389-400. <a href=\"https://dx.doi.org/10.3390/oceans3030026\" target=\"_blank\">https://dx.doi.org/10.3390/oceans3030026</a>","PeerRev":1},{"BRefID":352976,"RR":"<b>Borda, L.T.; Jadoul, Y.; Rasilo, H.; Casals, A.S.; Ravignani, A.</b> (2021). Vocal plasticity in harbour seal pups. <i>Phil. Trans. R. Soc. Lond. (B Biol. Sci.) 376(1840)</i>: 20200456. <a href=\"https://dx.doi.org/10.1098/rstb.2020.0456\" target=\"_blank\">https://dx.doi.org/10.1098/rstb.2020.0456</a>","PeerRev":1},{"BRefID":337270,"RR":"<b>Daems, P.-J.; Verstraeten, T.; Peeters, C.; Helsen, J.</b> (2021). Effects of wake on gearbox design load cases identified from fleet-wide operational data. <i>Forschung im Ingenieurwesen-Engineering Research 85</i>: 553-558. <a href=\"https://hdl.handle.net/10.1007/s10010-021-00444-3\" target=\"_blank\">https://hdl.handle.net/10.1007/s10010-021-00444-3</a>","PeerRev":1},{"BRefID":323200,"RR":"<b>Ravignani, A.</b> (2019). Timing of antisynchronous calling: a case study in a harbor seal pup (<i>Phoca vitulina</i>). <i>Journal of Comparative Psychology 133(2)</i>: 272-277. <a href=\"https://dx.doi.org/10.1037/com0000160\" target=\"_blank\">https://dx.doi.org/10.1037/com0000160</a>","PeerRev":1},{"BRefID":310384,"RR":"<b>Ravignani, A.; Kello, C.T.; de Reus, K.; Kotz, S.A.; Dalla Bella, S.; Méndez-Aróstegui, M.; Rapado-Tamarit, B.; Rubio-Garcia, A.; de Boer, B.</b> (2019). Ontogeny of vocal rhythms in harbor seal pups: an exploratory study. <i>Curr. Zool. 65(1)</i>: 107-120. <a href=\"https://dx.doi.org/10.1093/cz/zoy055\" target=\"_blank\">https://dx.doi.org/10.1093/cz/zoy055</a>","PeerRev":1},{"BRefID":391586,"RR":"<b>Verstraeten, T.; Nowe, A.; Keller, J.; Guo, Y.; Sheng, S.W.; Helsen, J.</b> (2019). Fleetwide data-enabled reliability improvement of wind turbines. <i>Renew. Sust. Energ. Rev. 109</i>: 428-437. <a href=\"https://dx.doi.org/10.1016/j.rser.2019.03.019\" target=\"_blank\">https://dx.doi.org/10.1016/j.rser.2019.03.019</a>","PeerRev":1},{"BRefID":293679,"RR":"<b>Ravignani, A.; Gross, S.; Garcia, M.; Rubio-Garcia, A.; de Boer, B.</b> (2017). How small could a pup sound? The physical bases of signaling body size in harbor seals. <i>Curr. Zool. 63(4)</i>: 457-465. <a href=\"https://dx.doi.org/10.1093/cz/zox026\" target=\"_blank\">https://dx.doi.org/10.1093/cz/zox026</a>","PeerRev":1},{"BRefID":285569,"RR":"<b>Ravignani, A.; Fitch, W.T.; Hanke, F.D.; Heinrich, T.; Hurgitsch, B.; Kotz, S.A.; Scharff, C.; Stoeger, A.S.; de Boer, B.</b> (2016). What pinnipeds have to say about human speech, music, and the evolution of rhythm. <i>Frontiers in Neuroscience 10</i>: 9 pp. <a href=\"https://dx.doi.org/10.3389/fnins.2016.00274\" target=\"_blank\">https://dx.doi.org/10.3389/fnins.2016.00274</a>","PeerRev":1},{"BRefID":438355,"RR":"<b>de Reus, K.</b> (2026). Vocal communication in harbour seal pups: Implications for language evolution. PhD Thesis. Van Marle: Hengelo. ISBN 978-94-92910-72-1. 228 pp.","PeerRev":0},{"BRefID":295838,"RR":"<b>Rodrigues, S.; Pinto, R.T.; Bauer, P.; Brys, T.; Nowé, A.</b> (2015). Online distributed voltage control of an offshore MIdc network using reinforcement learning, <b><i>in</i></b>: <i>2015 IEEE Congress on Evolutionary Computation (CEC): proceedings.</i> pp. 1769-1775. <a href=\"https://dx.doi.org/10.1109/CEC.2015.7257101\" target=\"_blank\">https://dx.doi.org/10.1109/CEC.2015.7257101</a>","PeerRev":0}],"conferences":[{"ConfID":3349,"ConfTitle":"TaZ#2024 - Wetenschap Aan Zee: Geheimen van de Noordzee"}],"datasets":null,"persons":[{"PersID":34842,"Surname":"de Boer","Firstname":"Bart","Initials":"B.","DirectorFlag":null,"MarineSciFlag":null,"SpecializedFlag":null,"Function":null},{"PersID":41234,"Surname":"Jadoul","Firstname":"Yannick","Initials":"Y.","DirectorFlag":null,"MarineSciFlag":null,"SpecializedFlag":null,"Function":null},{"PersID":44671,"Surname":"Nowé","Firstname":"Ann","Initials":null,"DirectorFlag":null,"MarineSciFlag":1,"SpecializedFlag":null,"Function":null},{"PersID":41235,"Surname":"Rasilo","Firstname":"Heikki","Initials":"H.","DirectorFlag":null,"MarineSciFlag":null,"SpecializedFlag":null,"Function":null}],"pastpers":[{"PersID":34841,"Surname":"Ravignani","Firstname":"Andrea","Initials":"A.","DirectorFlag":null,"MarineSciFlag":null,"SpecializedFlag":null,"Function":null}],"subpers":null,"projects":null,"urls":[{"URL":"https://ai.vub.ac.be/","externalID":null,"URLTypeCode":null,"URLType":"Institute home page"}],"pictures":[],"published":null,"affrefs":null,"collections":null,"thesterms":[{"ThesaurusTerm":"Renewable energy","ThestID":181519,"ThesTypID":29,"ThesType":"MOG Topics"},{"ThesaurusTerm":"Underwater noise & sound","ThestID":181524,"ThesTypID":29,"ThesType":"MOG Topics"}],"taxterms":null,"geoterms":null,"thestermsFRIS":[{"ThesaurusTerm":"Renewable energy","DutchTerm":"Hernieuwbare energie","ThestID":181519,"ThesTypID":29,"ThesType":"MOG Topics","Code":null},{"ThesaurusTerm":"Underwater noise & sound","DutchTerm":"Onderwatergeluid","ThestID":181524,"ThesTypID":29,"ThesType":"MOG Topics","Code":null}],"nXtins":null,"previns":null,"spcols":[{"SpColID":99,"SpName":"Marine expertise"},{"SpColID":121,"SpName":"Marine expertise: Type: Flemish university"}],"resmessage":"","complete":1,"participantrec":null,"peerrevs":11,"urlmaps":[]}