A partially-observable markov decision process for dealing with dynamically changing environments

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8206

Title:	A partially-observable markov decision process for dealing with dynamically changing environments
Authors:	Chatzis, Sotirios P. Kosmopoulos, Dimitrios I.
Major Field of Science:	Engineering and Technology
Field Category:	Computer and Information Sciences
Keywords:	POMDP problem;Bayesian non-parametrics;Dirichlet process
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).
URI:	https://hdl.handle.net/20.500.14279/8206
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
Affiliation :	Cyprus University of Technology Hellenic Mediterranean University
Appears in Collections:	Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

CORE Recommender

1

checked on Nov 6, 2023

393

Last Week
3

Last month
24

checked on Apr 28, 2024

Check