{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:36Z","timestamp":1729225716497,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>This paper introduces Dynamic Bayesian Optimisation for Multi-Arm Bandits (DBO-MAB), an algorithm that dynamically adapts hyperparameters of multi-arm bandit algorithms using incremental Bayesian optimisation. DBO-MAB addresses the challenge of tuning hyperparameters in uncertain and dynamic environments, particularly for applications like web server optimisation. It uses a dynamic range adjustment approach based on the interquartile mean (IQM) of observed rewards to focus the search space on promising regions. Evaluated across diverse static and dynamic environments, DBO-MAB outperforms state-of-the-art algorithms such as Bootstrapped UCB and f-Discounted-Sliding-Window Thompson Sampling, reducing average response time by \u224855%.<\/jats:p>","DOI":"10.3233\/faia240811","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:26:07Z","timestamp":1729171567000},"source":"Crossref","is-referenced-by-count":0,"title":["An Online Incremental Learning Approach for Configuring Multi-arm Bandits Algorithms"],"prefix":"10.3233","author":[{"given":"Mohammad Essa","family":"Alsomali","sequence":"first","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Rodrigues-Filho","sequence":"additional","affiliation":[{"name":"Department of Computing, Federal University of Santa Catarina, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leandro Soriano","family":"Marcolino","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barry","family":"Porter","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:26:07Z","timestamp":1729171567000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240811","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}