Autor: Furukawa, T. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Building a cognitive map using an SOM2
Autorzy:: Tokunaga, K.
Furukawa, T.
Powiązania:: https://bibliotekanauki.pl/articles/384201.pdf
Data publikacji:: 2010
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: self-organizing map
map building
place cells
head direction cells
autonomous robot
Opis:: In this paper, we propose a new method for building an environmental map in a self-organizing manner using visual information from a mobile robot. This method is based on a Higher Rank of Self-Organizing Map (SOM ), in which Kohonen’s SOM is extended to create a map of data distributions (set of manifolds). It is expected that the “SOM” is capable of creating an environmental map in a self-organizing manner from visual information, since the set of visual information obtained from each position in the environment forms a manifold at every position. We also show the effectiveness of the proposed method.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 2; 39-47
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Bottom-up learning of hierarchical models in a class of deterministic POMDP environments
Autorzy:: Itoh, H.
Fukumoto, H.
Wakuya, H.
Furukawa, T.
Powiązania:: https://bibliotekanauki.pl/articles/330566.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: partially observable system
Markov decision process
hierarchical model
bottom up learning
układ częściowo obserwowalny
decyzyjny proces Markowa
model hierarchiczny
Opis:: 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.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 597-615
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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