{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:09Z","timestamp":1761176289091,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Robust POMDPs extend classical POMDPs to incorporate model uncertainty using so-called uncertainty sets on the transition and observation functions, effectively defining ranges of probabilities. Policies for robust POMDPs must be (1) memory-based to account for partial observability and (2) robust against model uncertainty to account for the worst-case probability instances from the uncertainty sets. To compute such robust memory-based policies, we propose the pessimistic iterative planning (PIP) framework, which alternates between (1) selecting pessimistic POMDPs via worst-case probability instances from the uncertainty sets, and (2) computing finite-state controllers (FSCs) for these pessimistic POMDPs. Within PIP, we propose the RFSCNET algorithm, which optimizes a recurrent neural network to compute the FSCs. The empirical evaluation shows that RFSCNET can compute better-performing robust policies than several baselines and a state-of-the-art robust POMDP solver.<\/jats:p>","DOI":"10.3233\/faia251391","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:00:15Z","timestamp":1761127215000},"source":"Crossref","is-referenced-by-count":0,"title":["Pessimistic Iterative Planning with RNNs for Robust POMDPs"],"prefix":"10.3233","author":[{"given":"Maris F.L.","family":"Galesloot","sequence":"first","affiliation":[{"name":"Radboud University Nijmegen, NL"}]},{"given":"Marnix","family":"Suilen","sequence":"additional","affiliation":[{"name":"University of Antwerp \u2013 Flanders Make, BE"}]},{"given":"Thiago D.","family":"Sim\u00e3o","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, NL"}]},{"given":"Steven","family":"Carr","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, US"}]},{"given":"Matthijs T.J.","family":"Spaan","sequence":"additional","affiliation":[{"name":"Delft University of Technology, NL"}]},{"given":"Ufuk","family":"Topcu","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, US"}]},{"given":"Nils","family":"Jansen","sequence":"additional","affiliation":[{"name":"Ruhr-University Bochum, DE"},{"name":"Radboud University Nijmegen, NL"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:00:16Z","timestamp":1761127216000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251391","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}