| Téléchargement | - Voir la version finale : Dynamic programming with incomplete information to overcomenavigational uncertainty in POMDPs (PDF, 1.6 Mio)
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| Lien | https://caiac.pubpub.org/pub/qdmqsaj7 |
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| Auteur | Rechercher : Beeler, Chris1; Rechercher : Li, Xinkai Li; Rechercher : Bellinger, Colin1; Rechercher : Crowley, Mark; Rechercher : Fraser, Maia; Rechercher : Tamblyn, Isaac |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | The 37th Canadian Conference on Artificial Intelligence (Canadian AI 2024), May 27-31, 2024, Guelph, Ontario, Canada |
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| Sujet | dynamic programming; partially observable; Markov decision processes; risk management; controlled sensing |
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| Résumé | Using a generalizable novel nautical navigation environment, we show how dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known. By incorporating uncertainty into our model, we show that navigation policies can be constructed that maintain safety, outperforming the baseline performance of traditional dynamic programming for Markov decision processes (MDPs). Adding in controlled sensing methods, we show that these policies can also lower measurement costs at the same time. |
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| Date de publication | 2024-05-27 |
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| Maison d’édition | Canadian Artificial Intelligence Association |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | aa2e9fc6-dbcb-4a14-a604-9d0951bb7183 |
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| Enregistrement créé | 2024-06-13 |
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| Enregistrement modifié | 2024-06-14 |
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