Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Anthony R. Cassandra"'
Publikováno v:
Artificial Intelligence. 101:99-134
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::989166f8f2b1db522d5dc5a6a362d5da
https://doi.org/10.1016/s0004-3702(98)00023-x
https://doi.org/10.1016/s0004-3702(98)00023-x
Autor:
Bruce Bargmeyer, Anthony R. Cassandra, Sam Chance, Alfred de Jager, Bruno Felluga, Jerry Fowler, Manfred Grasserbauer, Palle Haastrup, Xuan Huang, Stefan Jensen, Ryan J. Lozado, Athanassios Papageorgiou, Greg Pitts, Paolo Plini, Stefan Poslad, Dominique Preux, François-Xavier Prunayre, Erika Rimaviciute, Ole Sortkjær, Michael Stjernholm, Guy Weets, Jørgen Würtz, Landong Zuo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1be37c172448ed50ecf794a25e0b8fb
https://doi.org/10.1016/b978-044452973-2/50003-8
https://doi.org/10.1016/b978-044452973-2/50003-8
Publikováno v:
IROS
Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5febae5ef9ad54e6efd7f8b54af3daf3
https://doi.org/10.1109/iros.1996.571080
https://doi.org/10.1109/iros.1996.571080
Publikováno v:
ICML
Partially observable Markov decision processes (POMDP's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of POMDP's is motivated by a need to address realisti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8db733b459cc727765f427352fa3bbac
https://doi.org/10.1016/b978-1-55860-377-6.50052-9
https://doi.org/10.1016/b978-1-55860-377-6.50052-9
Publikováno v:
KI-95: Advances in Artificial Intelligence ISBN: 9783540603436
KI
Reasoning with Uncertainty in Robotics ISBN: 9783540613763
Reasoning with Uncertainty in Robotics
KI
Reasoning with Uncertainty in Robotics ISBN: 9783540613763
Reasoning with Uncertainty in Robotics
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. In many cases, we have developed new ways of viewing the problem that are, perhaps, more consis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28ab2197651b65017169a868fb4b308a
https://doi.org/10.1007/3-540-60343-3_22
https://doi.org/10.1007/3-540-60343-3_22
Autor:
Glenn Carroll, John Adcock, Anthony R. Cassandra, John McCann, Michael L. Littman, Yoshihiko Gotoh, Eugene Charniak, Jeremy Katz
Publikováno v:
Artificial Intelligence. (1-2):45-57
We consider what tagging models are most appropriate as front ends for probabilistic context-free grammar parsers. In particular, we ask if using a “multiple tagger”, a tagger that returns more than one tag, improves parsing performance. Our conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6623ea69875cfe6884dbd341190084cc
Conference
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Conference
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