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|Title:||A partially-observable markov decision process for dealing with dynamically changing environments||Authors:||Chatzis, Sotirios P.
Kosmopoulos, Dimitrios I.
|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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