Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8206
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
Issue Date: 2014
Source: 10th International Conference on Artificial Intelligence Applications and Innovations, 2014, Island of Rhodes, Greece, 19-21 September
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
URI: http://172.16.21.8/jspui/handle/10488/8206
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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