Object
Title: Bottom-up learning of hierarchical models in a class of deterministic POMDP environments
Contributor:
Byrski, Aleksander - ed. ; Kisiel-Dorohinicki, Marek - ed. ; Dobrowolski, Grzegorz - ed.
Subtitle:
Group publication title:
Abstract:
The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. ; However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations) can be learned at least in a class of deterministic POMDP environments.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 25, number 3 (2015) ; click here to follow the link
Language:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2015)
Last modified:
May 6, 2024
In our library since:
May 6, 2024
Number of object content hits:
49
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/88905
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International Journal of Applied Mathematics and Computer Science (AMCS)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 26 (2016)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25 (2015)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25, number 1 (2015)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25, number 2 (2015)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25, number 3 (2015)
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| Edition name | Date |
|---|---|
| Bottom-up learning of hierarchical models in a class of deterministic POMDP environments | May 6, 2024 |
