Curriculum Vitaes
Profile Information
- Affiliation
- Musashino University
- Degree
- 政策・メディア(慶應義塾大学)
- J-GLOBAL ID
- 201301073774840492
- researchmap Member ID
- B000230846
慶應義塾大学大学院政策・メディア研究科後期博士課程修了．玉川大学脳科学研究所嘱託研究員を経て，東京工科大学コンピュータサイエンス学部助教，産業技術大学院大学産業技術研究科情報アーキテクチャ専攻助教，武蔵野大学データサイエンス学部・教育部会准教授．博士(政策・メディア)．
専門は知覚情報処理，神経情報処理，認知科学．視覚情報処理に関係する神経細胞のモデル化と，計算機によるシミュレーション，また近年応用研究として人間の意図を推定し，行動を支援するインタフェースの開発，更に知能を持ったロボットの実現を目指したロボカップへも出場している．
専門は知覚情報処理，神経情報処理，認知科学．視覚情報処理に関係する神経細胞のモデル化と，計算機によるシミュレーション，また近年応用研究として人間の意図を推定し，行動を支援するインタフェースの開発，更に知能を持ったロボットの実現を目指したロボカップへも出場している．
Papers
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知能と情報(CD-ROM), 36(4), 2024
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2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), Dec, 2022
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Applied Sciences, 12(17) 8720-8720, Aug 31, 2022Human cooperative behavior includes passive action strategies based on others and active action strategies that prioritize one’s own objective. Therefore, for cooperation with humans, it is necessary to realize a robot that uses these strategies to communicate as a human would. In this research, we aim to realize robots that evaluate the actions of their opponents in comparison with their own action strategies. In our previous work, we obtained a Meta-Strategy with two action strategies through the simulation of learning between agents. However, humans’ Meta-Strategies may have different characteristics depending on the individual in question. In this study, we conducted a collision avoidance experiment in a grid space with agents with active and passive strategies for giving way. In addition, we analyzed whether a subject’s action changes when the agent’s strategy changes. The results showed that some subjects changed their actions in response to changes in the agent’s strategy, as well as subjects who behaved in a certain way regardless of the agent’s strategy and subjects who did not divide their actions. We considered that these types could be expressed in terms of differences in Meta-Strategies, such as active or passive Meta-Strategies for estimating an opponent’s strategy. Assuming a human Meta-Strategy, we discuss the action strategies of agents who can switch between active and passive strategies.
Misc.
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Musashino University Smart Intelligence Center 紀要, 5 101-119, Mar 31, 2024
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Musashino University Smart Intelligence Center 紀要, 5 91-100, Mar 31, 2024
Books and Other Publications
2Presentations
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Proceedings of the Annual Conference of JSAI, 2022, The Japanese Society for Artificial IntelligenceIn humans' cooperative behavior, there are two types of behavioral strategies: passive behavioral strategies based on the others, and active behavioral strategies based on the objective-first. In order to realize a robot that can use different strategies and communicate like a person, we created an agent that can switch between active and passive strategies. However, it is not clear whether people change their own behavioral strategies according to each strategy. In this study, we conducted an experiment in which agents with multiple strategies of actively giving way and passively giving way passed each other in a grid-like space, and analyzed whether people's behavior changed when the agents' strategies changed. The results show that, in addition to subjects who change their own behavior in response to changes in the agent's strategy, there are also subjects who behave in a certain way regardless of the agent's strategy and subjects whose behavior is not clearly divided.
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Proceedings of the Annual Conference of JSAI, 2021, The Japanese Society for Artificial IntelligenceIn human's cooperative behavior, there are some strategies: a passive behavioral strategy based on others’behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether subject's behavior changes when agent's strategy changes. We compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the oncoming agent.
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人工知能学会全国大会(Web), 2020, The Japanese Society for Artificial Intelligence<p>Understanding of human flexible cooperative behavior in groups is important for constructing intelligent systems cooperate with human. In group behavior, top-down action selection based on sharing the participants' intention and bottom-down intention adjustment based on estimated intention from each behavior are considered to be done. To construct agent models realizing such process, we have analyzed the timing of dynamical change of intention and strategies of adjusting each intention in the group using pattern task which abstracting the group behavior including cooperative selection of intention and action for achieving the particular goal. In this research, we construct an agent model based on the analysis results and check the action selection process of each strategies using model simulations.</p>
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Proceedings of the Fuzzy System Symposium, 2019, Japan Society for Fuzzy Theory and Intelligent Informatics<p>In order to understand human flexible cooperative behavior in a group, we have developed a cooperative pattern task requiring switching of intention and behavior. Through the analysis of the behavioral experiment using the task, subjects' optimal behavior to a shared target in minimal steps and majority voting based action selection are clarified. In this research, agent models including "self- preferred selection" and "estimation of other agents' intention" are constructed to reveal implicitly shared unobservable strategy under the subjects' behavior. Moreover, we analyze the subjects' action selection process by model-based agent simulation and comparison between subjects' and agents' behavior.</p>
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Proceedings of the Fuzzy System Symposium, 2019, Japan Society for Fuzzy Theory and Intelligent Informatics<p>We have been modeled based on real soccer data analysis in RoboCup Soccer Simulation 2D. In the case of a soccer game, it is considered that soccer players share cooperative patterns in a pass scene. In this research, we analyze the positional relationship between teammate and opponent team member using Delaunay Triangulation. Furthermore, the key player which receives a pass is extracted from the gaze direction of the ball holder.</p>
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Proceedings of the Fuzzy System Symposium, 2019, Japan Society for Fuzzy Theory and Intelligent Informatics<p>In our cooperative behavior, there are two type strategies passive behavioral strategies based on others' behavior and active behavioral strategies that oneself acts first. In order to realize a robot that can communicate with human, it is necessary to acquire such behavioral strategies. However, it is not clear how to acquire meta-strategy to switch those strategies. In this study, we have experimented with multi-agent collision avoidance simulations as an example of cooperative tasks. In the experiment, we have used reinforcement learning to obtain an active and passive strategy by rewarding the interaction with agents facing each other. Additionally, we have acquired meta-strategy by reinforcement learning to selectively use those strategies and evaluated them.</p>
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Proceedings of the Annual Conference of JSAI, 2019<p>We have a behavior experiment using pattern task abstracting cooperative behaviors that require intention estimation and action switching to specific goals. And we have analyzed strategies to adjust cooperative intention estimations. In this research, we constructed an agent model that have three strategies of "random selection", "self-priority selection", and "other agent's target pattern estimation". And the decision making process was verified by simulation.</p>
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電子情報通信学会大会講演論文集(CD-ROM), Mar 6, 2018
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Proceedings of the Annual Conference of JSAI, 2018<p>In a goal type ball game such as soccer and handball, a plurality of players who can pass are searched for, and each player's intention is estimated and a player who can pass is selected. Furthermore, it con rms the position and behavior of enemies that exist around passable players, estimates their intentions, and determines teammate players who pass the pass most successfully. In order to realize instantaneous intention estimation and judgment subject to strong temporal and spatial constraints, cooperative patterns shared within the group are considered to exist. Therefore, in this research, we presented to subjects a rst-person perspective of professional soccer players by using virtual environment, and analyzed the gaze behaviors pre- and post-training. Based on the results, we discuss that subjects switch their behavior by estimating the intention of otherplayers who pass and receive to subject by presenting the visual information based on the rst-person perspective.</p>
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Proceedings of the Fuzzy System Symposium, 2018<p>In recent cooperative behavior study with goal type sports, some devices such as action cams and acceleration sensors are used for measuring personal behaviors. In these measurements, we mainly analyze the output of player's behavior. But in cooperative behavior, self and other persons confirm mutual actions and share their intention estimation. And they decide their purpose and judge the next own action. In this research we analyze the intention estimation by player's head movement and model it to simulate the estimation process. We experimented soccer pass behavior and measure player's head movement by a flying drone. We describe the measurement accuracy of the player's head movement by the drone and the analysis result of the timing when a ball holder shares the intention of other players and passing.</p>
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Proceedings of the Japan Joint Automatic Control Conference, Nov 10, 2017
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知識ベースシステム研究会, May 22, 2017
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Model Analysis of Human Decision Making Process with Intention Inference in Cooperative Pattern Task電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, Mar 21, 2017
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Model Analysis of Human Decision Making Process with Intention Inference in Cooperative Pattern Task電子情報通信学会技術研究報告, Mar 14, 2017
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人工知能学会全国大会論文集(CD-ROM), 2017
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人工知能学会全国大会論文集(CD-ROM), 2017
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Proceedings of the Fuzzy System Symposium, 2017<p> </p>
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Proceedings of the Fuzzy System Symposium, 2017<p> </p>
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電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, Mar 22, 2016
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電子情報通信学会技術研究報告, Mar 15, 2016
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Meeting Abstracts of the Physical Society of Japan, 2016
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Proceedings of the Fuzzy System Symposium, 2016<p> </p>
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IEICE technical report. Neurocomputing, Mar 16, 2015We have implemented the personal mobility performing semiautonomous control by estimating the avoidance direction of pedestrian and the avoidance judgment timing of passenger. In the space of pedestrian and personal mobility coexist, it is possible to realize a safety collision avoidance by moving the mobility in the lateral before passenger's avoidance judgment. Therefore, we utilize a Microsoft Kinect which obtains the coordinates of both feet of pedestrian. And we have tested collision avoidance experiments between a pedestrian and the mobility with the implemented model estimating avoiding direction from the feet's relative positions. We have evaluated the pupil size and the number of saccades of passenger by eye camera. As a result, it is possible to reduce the mental stress by semiautonomous control of mobility than autonomous control. Furthermore, we have evaluated the important body site in avoiding judgment and avoidance judgment timing. Based on the results, we have proposed a model of passenger's vision guidance and mobility avoidance.
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IEICE technical report. ME and bio cybernetics, Mar 16, 2015We have implemented the personal mobility performing semiautonomous control by estimating the avoidance direction of pedestrian and the avoidance judgment timing of passenger. In the space of pedestrian and personal mobility coexist, it is possible to realize a safety collision avoidance by moving the mobility in the lateral before passenger's avoidance judgment. Therefore, we utilize a Microsoft Kinect which obtains the coordinates of both feet of pedestrian. And we have tested collision avoidance experiments between a pedestrian and the mobility with the implemented model estimating avoiding direction from the feet's relative positions. We have evaluated the pupil size and the number of saccades of passenger by eye camera. As a result, it is possible to reduce the mental stress by semiautonomous control of mobility than autonomous control. Furthermore, we have evaluated the important body site in avoiding judgment and avoidance judgment timing. Based on the results, we have proposed a model of passenger's vision guidance and mobility avoidance.
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電子情報通信学会技術研究報告, Mar 9, 2015
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人工知能学会全国大会論文集, 2015<p>ロボカップサッカーシミュレーション2Dのログデータに対し、帰納論理プログラミングシステムProgolを適用し、有効な攻撃パターンの発見・抽出を試みた．まずログデータからパスやドリブル等の行動を抽出し，述語論理形式で記述する．次に、それらの行動に対してロングパスやサイドパス等の情報を背景知識として定義・付与する．その後、効果的な行動の遷移関係をもつ仮説を推論するための論理設定について検討・評価する．</p>
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IEICE technical report. Neurocomputing, Mar 17, 2014Recent physiological studies have indicated that visual attention modulates neural activity depending on the cell types in V4. Specifically, narrow spiking (NS) neurons and broad spiking (BS) neurons, which are classified according to the spiking waveforms, have both reported to exhibit increased firing rate and less inter-trial variations for attention. We simulated the NS and BS neural activity for visual attention using a neural network model based on spontaneous activity, under the assumption that the visual attention modulation would be shown by the change in variance of input firing rate distributed with a lognormal distribution. Consistent with the physiological studies, an increase in firing rate and a decrease in inter-trial variance was simultaneously obtained in simulations by increasing variance of input firing rate distribution. These results indicate that visual attention forms strong sparse and weak dense input or a ' winner-take-all' state, to improve the signal-to-noise ratio of the target information.
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電子情報通信学会技術研究報告, Mar 10, 2014
Research Projects
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科学研究費助成事業, 日本学術振興会, Apr, 2024 - Mar, 2027
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科学研究費助成事業 若手研究(B), 日本学術振興会, Apr, 2016 - Mar, 2020
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Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Japan Society for the Promotion of Science, Apr, 2013 - Mar, 2016
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Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), Japan Society for the Promotion of Science, Apr, 2012 - Mar, 2016
Social Activities
1Media Coverage
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Aidemy, https://business.aidemy.net/ai-can/musashino_univercity/, May, 2024 Internet
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paizaラーニング, https://paiza.jp/feedback/musashino-u, Apr, 2024 Internet
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PR TIMES, https://prtimes.jp/main/html/rd/p/000000064.000067788.html, Oct, 2021 Internet
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毎日新聞@Press, https://mainichi.jp/articles/20201225/pls/00m/020/107000c, Dec, 2020 Newspaper, magazine