Decision maker based on atomic switches
We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated ``decision-making'' capability that is known to be one...
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doaj-db4a1d50858748f1b583e209c7baf8dc2020-11-25T01:23:21ZengAIMS PressAIMS Materials Science2372-04842016-02-013124525910.3934/matersci.2016.1.245matersci-03-00245Decision maker based on atomic switchesSong-Ju Kim0Tohru Tsuruoka1Tsuyoshi Hasegawa2Masashi AonoKazuya Terabe3Masakazu Aono4WPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305–0044, JapaWPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305–0044, JapaDepartment of Applied Physics, Waseda University, 3-4-1 Ookubo, Shinjuku-ku, Tokyo 169-8555, JapaWPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305–0044, JapaWPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305–0044, JapaWe propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated ``decision-making'' capability that is known to be one of the most important intellectual abilities in human beings. We considered a popular decision-making problem studied in the context of reinforcement learning, the multi-armed bandit problem (MAB); the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by each volume of precipitated metal atoms, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. The ``tug-of-war (TOW) dynamics'' of the ASDM exhibits higher efficiency than conventional reinforcement-learning algorithms. We show analytical calculations that validate the statistical reasons for the ASDM to produce such high performance, despite its simplicity. Efficient MAB solvers are useful for many practical applications, because MAB abstracts a variety of decision-making problems in real-world situations where an efficient trial-and-error is required. The proposed scheme will open up a new direction in physics-based analog-computing paradigms, which will include such things as ``intelligent nanodevices'' based on self-judgment.http://www.aimspress.com/Materials/article/650/fulltext.htmlnatural computingatomic switchtug-of-war dynamicsamoeba-inspired computingmulti-armed bandit problemreinforcement learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Song-Ju Kim Tohru Tsuruoka Tsuyoshi Hasegawa Masashi Aono Kazuya Terabe Masakazu Aono |
spellingShingle |
Song-Ju Kim Tohru Tsuruoka Tsuyoshi Hasegawa Masashi Aono Kazuya Terabe Masakazu Aono Decision maker based on atomic switches AIMS Materials Science natural computing atomic switch tug-of-war dynamics amoeba-inspired computing multi-armed bandit problem reinforcement learning |
author_facet |
Song-Ju Kim Tohru Tsuruoka Tsuyoshi Hasegawa Masashi Aono Kazuya Terabe Masakazu Aono |
author_sort |
Song-Ju Kim |
title |
Decision maker based on atomic switches |
title_short |
Decision maker based on atomic switches |
title_full |
Decision maker based on atomic switches |
title_fullStr |
Decision maker based on atomic switches |
title_full_unstemmed |
Decision maker based on atomic switches |
title_sort |
decision maker based on atomic switches |
publisher |
AIMS Press |
series |
AIMS Materials Science |
issn |
2372-0484 |
publishDate |
2016-02-01 |
description |
We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total number of metal atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated ``decision-making'' capability that is known to be one of the most important intellectual abilities in human beings. We considered a popular decision-making problem studied in the context of reinforcement learning, the multi-armed bandit problem (MAB); the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by each volume of precipitated metal atoms, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. The ``tug-of-war (TOW) dynamics'' of the ASDM exhibits higher efficiency than conventional reinforcement-learning algorithms. We show analytical calculations that validate the statistical reasons for the ASDM to produce such high performance, despite its simplicity. Efficient MAB solvers are useful for many practical applications, because MAB abstracts a variety of decision-making problems in real-world situations where an efficient trial-and-error is required. The proposed scheme will open up a new direction in physics-based analog-computing paradigms, which will include such things as ``intelligent nanodevices'' based on self-judgment. |
topic |
natural computing atomic switch tug-of-war dynamics amoeba-inspired computing multi-armed bandit problem reinforcement learning |
url |
http://www.aimspress.com/Materials/article/650/fulltext.html |
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