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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 7 of 7  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
AI 2019-07-22
16:30		 Hokkaido   Modeling of Cyber Attack Based on POMDP
Kazuma Igami, Hirofumi Yamaki (Tokyo Denki Univ.) AI2019-15		 APTs(Advanced Persistent Threats), which are a type of cyber-attack, are a major threat to information systems because t... [more] AI2019-15
pp.77-82
IBISML 2016-11-17
14:00		 Kyoto Kyoto Univ. Approximate Value Iteration Algorithms for Partially Observable Markov Decision Processes in Geometric Dual Representation
Hiroshi Tsukahara, Mitsuru Anbai, Makoto Oobayashi (Denso IT Lab.) IBISML2016-71		 We propose new approximate algorithms for the value iteration of partially observable Markov decision
processes (POMDPs... [more]		 IBISML2016-71
pp.177-184
NC, NLP 2013-01-24
10:50		 Hokkaido Hokkaido University Centennial Memory Hall Significance of non-stationary of dynamics for learning cooperative behavior
Akihiro Tawa, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NLP2012-108 NC2012-98		 To understand how cooperative behaviors emerge is important in the field of multi-agent system research. Although this e... [more] NLP2012-108 NC2012-98
pp.25-30
IBISML 2012-03-12
14:40		 Tokyo The Institute of Statistical Mathematics Kernel Bellman Equations in POMDPs
Yu Nishiyama (ISM), Abdeslam Boularias (MPI), Arthur Gretton (UCL), Kenji Fukumizu (ISM) IBISML2011-92		 We propose to handle POMDPs in reproducing kernel Hilbert spaces (RKHSs) using recent kernel methods of embedding distri... [more] IBISML2011-92
pp.35-42
IBISML 2012-03-12
15:30		 Tokyo The Institute of Statistical Mathematics Apprenticeship Learning for Model Parameters of Partially Observable Environments
Takaki Makino (Univ. of Tokyo), Johane Takeuchi (HRI-JP) IBISML2011-94		 We consider apprentice learning, i.e., to make an agent learn a task by observing an expert demonstrating the task, in a... [more] IBISML2011-94
pp.49-54
NC, IPSJ-BIO [detail] 2011-06-24
16:30		 Okinawa 50th Anniversary Memorial Hall, University of the Ryukyus Solving POMDPs using Restricted Boltzmann Machines with Echo State Networks
Makoto Otsuka, Junichiro Yoshimoto, Stefan Elfwing, Kenji Doya (OIST) NC2011-19		 A partially observable Markov decision process (POMDP) can be solved in a model-based way using explicit knowledge of th... [more] NC2011-19
pp.143-148
AI, KEWPIE 2004-07-29
13:00		 Kyoto Keihanna Plaza SAPS: The Exploitation Reinforcement Learning Method on POMDPs
Wataru Uemura, Atsushi Ueno, Shoji Tatsumi (Osaka City Univ.) 		This paper proposes the Episode Profit Sharing(EPS) that can estimate the received rewards on partially observable marko... [more] AI2004-12
pp.1-5
 Results 1 - 7 of 7  /   
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