Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/isamsr53229.2021.9567835

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s42979-020-00191-2

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Other Link： http://link.springer.com/article/10.1007/s42979-020-00191-2/fulltext.html

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Publisher：Springer  

DOI： 10.1007/978-3-030-22660-2_34

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Publisher：IEEE  

DOI： 10.1109/CEC.2019.8790087

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-03098-8_54

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/jcmsi.11.331

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.9746/jcmsi.11.321

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Publisher：Springer  

DOI： 10.1007/978-3-319-92043-6_52

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Publisher：ACM  

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Other Link： http://dblp.uni-trier.de/db/conf/gecco/gecco2018c.html#conf/gecco/UwanoDTK18

Publisher：ACM  

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Other Link： http://dblp.uni-trier.de/db/conf/gecco/gecco2018c.html#conf/gecco/UmenaiUST18

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Publisher：Springer  

DOI： 10.1007/978-3-030-03098-8_54

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Language：English  

DOI： 10.9746/jcmsi.11.32

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Language：Japanese   Publisher：一般社団法人 日本航空宇宙学会  

This paper focuses on the Evolutional Triangle Similarity Matching (ETSM) method for estimating spacecraft location in Smart Lander for Investigating Moon (SLIM) mission and improves it by adding the functions of elimination of line symmetric triangles between crater map and camera shot image, comparison of rotation relationship of triangles and triangle formation method using Delaunay triangulation and introducing point group matching as a coordinate calculation function. To evaluate the robustness of the improved method, we conduct simulation experiments using the crater map and camera shot images in six situations. This experiments have revealed the following implications: (1) this method improved accuracy of location estimation within 5.1 pixels by the functions of elimination of line symmetric triangles between crater map and camera shot image, (2) this method slight got worse accuracy at low or high altitude of spacecraft, however, this method successfully reduced incorrect spacecraft location estimation by comparison of rotation relationship of triangles, (3) this method improved accuracy of location estimation by triangle formation method using Delaunay triangulation, but possibility of incorrect spacecraft location estimation is slight increased, and (4) integration method of these three mechanism can estimate spacecraft location within 5 pixels without being affected altitude difference and rotation of camera shot image.

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Publisher：AAAI Press  

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Other Link： http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2018.html#conf/aaaiss/UwanoT18

Publisher：AAAI Press  

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Other Link： http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2018.html#conf/aaaiss/TobaruUIMTTUT18

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press  

Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA. To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park
and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints
and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.

DOI： 10.20965/jrm.2017.p0877

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press  

This paper proposes a learning goal space that visualizes the distribution of the obtained solutions to support the exploration of the learning goals for a learner. Subsequently, we examine the method for assisting a learner to present the novelty of the obtained solution. We conduct a learning experiment using a continuous learning task to identify various solutions. To assign the subjects space to explore the learning goals, several parameters related to the success of the task are not instructed to the subjects. In the comparative experiment, three types of learning feedbacks provided to the subjects are compared. These are presenting the learning goal space with obtained solutions mapped on it, directly presenting the novelty of the obtained solutions mapped on it, and presenting some value that is slightly related to the obtained solution. In the experiments, the subjects to whom the learning goal space or novelty of the obtained solution is shown, continue to identify solutions according to their learning goals until the final stage in the experiment is attained. Therefore, in a continuous learning task, our supporting method of directly or indirectly presenting the novelty of the obtained solution through the learning goal space is effective.

DOI： 10.20965/jaciii.2017.p0907

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press  

This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL)
it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL)
it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. To compare the two methods, we designed experiments by adjusting the following maze factors: (1) the location of the start point and goal
(2) the number of agents
and (3) the size of maze. The intensive simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change. The PMRL mechanism always enables the agents to learn cooperative behavior, whereas the YARL mechanism makes the agents learn cooperative behavior over a small number of learning iterations. In zero communication, multi-agent cooperation, it is important that only agents that have a conflict cooperate with each other.

DOI： 10.20965/jaciii.2017.p0917

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

Cuckoo Search (CS) is the powerful optimization algorithm and has been researched recently. Cuckoo Search for Dynamic Environment (D-CS) has proposed and tested in dynamic environment with multi-modality and cyclically before. It was clear that has the hold capability and can find the optimal solutions in this environment. Although these experiments only provide the valuable results in this environment, D-CS not fully explored in dynamic environment with other dynamism. We investigate and discuss the find and hold capabilities of D-CS on dynamic environment with randomness. We employed the multi-modal dynamic function with randomness and applied D-CS into this environment. We compared D-CS with CS in terms of getting the better fitness. The experimental result shows the D-CS has the good hold capability on dynamic environment with randomness. Introducing the Local Solution Comparison strategy and Concurrent Solution Generating strategy help to get the hold and find capabilities on dynamic environment with randomness.

DOI： 10.1007/978-3-319-61824-1_62

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER INT PUBLISHING AG  

This paper introduces a reinforcement learning technique with an internal reward for a multi-agent cooperation task. The proposed method is an extension of Q-learning which changes the ordinary (external) reward to the internal reward for agent-cooperation under the condition of no communication. To increase the certainty of the proposed methods, we theoretically investigate what values should be set to select the goal for the cooperation among agents. In order to show the effectiveness of the proposed method, we conduct the intensive simulation on the maze problem for the agent-cooperation task, and confirm the following implications: (1) the proposed method successfully enable agents to acquire cooperative behaviors while a conventional method fails to always acquire such behaviors; (2) the cooperation among agents according to their internal rewards is achieved no communication; and (3) the condition for the cooperation among any number of agent is indicated.

DOI： 10.1007/978-3-319-49049-6_33

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

This paper focuses on a multi-Agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-Agent cooperation without sufficient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate actions by changing internal rewards given to the agents, and extends the reinforcement learning methods (Q-learning and Profit Sharing) to enable the agents to acquire the appropriate Q-values up- dated according to the derived condition. Concretely, the internal rewards change when the agents can only find better solution than the current one. The intensive simulations on the maze problems as one of test beds have revealed the following implications:(1) our proposed method successfully enables the agents to select their own appropriate cooperating actions which contribute to acquiring the minimum steps towards to their goals, while the conventional methods (i.e., Q-learning and Profit Sharing) cannot always acquire the minimum steps
and (2) the proposed method based on Profit Sharing provides the same good performance as the proposed method based on Q-learning.

DOI： 10.4108/eai.3-12-2015.2262878

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.

DOI： 10.1109/CEC.2016.7744001

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Publisher：AAAI Press  

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Other Link： http://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2016.html#conf/aaaiss/HaradaUKTKMT16

Publisher：ICST/ACM  

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Other Link： http://dblp.uni-trier.de/db/conf/bionetics/bict2015.html#conf/bionetics/UwanoTNTK15

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese  

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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DOI： 10.2322/astj.JSASS-D-17-00011

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CiNii Article

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Language：Japanese   Publisher：人工知能学会 ; 2014-  

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Language：Japanese  

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Language：Japanese   Publisher：人工知能学会 ; 2014-  

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Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in realtime, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

Scopus

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Language：English   Publisher：IEEE  

This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.

DOI： 10.1109/CEC.2016.7744001

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J-GLOBAL

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J-GLOBAL

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