Please use this identifier to cite or link to this item:
Title: A partially-observable markov decision process for dealing with dynamically changing environments
Authors: Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios I. 
Keywords: POMDP problem;Bayesian non-parametrics;Dirichlet process
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: 2014
Source: 10th International Conference on Artificial Intelligence Applications and Innovations, 2014, Rhodes, Greece, 19-21 September, pp. 111-120
Conference: IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations 
Abstract: This paper offers a solution to the non-stationary POMDP problem, by making use of methods and concepts from the field of Bayesian non-parametrics, specifically dynamic hierarchical Dirichlet process priors. We combine block Gibbs sampling and importance sampling to perform inference. We evaluate the method in several benchmark policy learning tasks
Description: Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 436).
ISBN: 978-3-662-44654-6 (online)
978-3-662-44653-9 (print)
DOI: 10.1007/978-3-662-44654-6_11
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s)

Last Week
Last month
checked on Aug 18, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.