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SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale gen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028621/ https://www.ncbi.nlm.nih.gov/pubmed/29997493 http://dx.doi.org/10.3389/fnbot.2018.00025 |
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author | Nakamura, Tomoaki Nagai, Takayuki Taniguchi, Tadahiro |
author_facet | Nakamura, Tomoaki Nagai, Takayuki Taniguchi, Tadahiro |
author_sort | Nakamura, Tomoaki |
collection | PubMed |
description | To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inferences easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environment and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, the connected modules are dependent on each other and their parameters must be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it has become harder to derive and implement equations of large-scale models. Thus, in this paper, we propose a parameter estimation method that communicates the minimum parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed. |
format | Online Article Text |
id | pubmed-6028621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60286212018-07-11 SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model Nakamura, Tomoaki Nagai, Takayuki Taniguchi, Tadahiro Front Neurorobot Neuroscience To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inferences easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environment and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, the connected modules are dependent on each other and their parameters must be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it has become harder to derive and implement equations of large-scale models. Thus, in this paper, we propose a parameter estimation method that communicates the minimum parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed. Frontiers Media S.A. 2018-06-26 /pmc/articles/PMC6028621/ /pubmed/29997493 http://dx.doi.org/10.3389/fnbot.2018.00025 Text en Copyright © 2018 Nakamura, Nagai and Taniguchi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Nakamura, Tomoaki Nagai, Takayuki Taniguchi, Tadahiro SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title | SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title_full | SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title_fullStr | SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title_full_unstemmed | SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title_short | SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model |
title_sort | serket: an architecture for connecting stochastic models to realize a large-scale cognitive model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028621/ https://www.ncbi.nlm.nih.gov/pubmed/29997493 http://dx.doi.org/10.3389/fnbot.2018.00025 |
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