Toyoaki Nishida

Kyoto University

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Yasser Mohammad

NEC (Japan)

Yoshimasa Ohmoto

Shizuoka University

Hung‐Hsuan Huang

University of Fukuchiyama

Yukiko Nakano

Seikei University

Yasuyuki Sumi

Future University Hakodate

Atsushi Nakazawa

Okayama University

Hidekazu Kubota

National Institute of Advanced Industrial Science and Technology

Shuji Doshita

Ryukoku University

AČ

Aleksandra Čereković

University of Zagreb

Shogo Okada

Japan Advanced Institute of Science and Technology

Cooperative Institutions

Kyoto University

University of Naples Federico II

Indian Institute of Technology Delhi

Zhejiang University

Universidad Nacional Autónoma de México

DSI Informationstechnik (Germany)

The University of Tokyo

Shanghai Jiao Tong University

Beijing Jiaotong University

National University of Singapore

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Causality Analysis

Advanced information and knowledge processing (2015)

Yasser Mohammad Toyoaki Nishida

The study of causalityCausality can be traced back to Aristotle who defined four types of causal relations (material, formal, efficient and final causes).

Causality

10.1007/978-3-319-25232-2_5

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Social intelligence design in ambient intelligence

AI & Society (2009)

Anton Nijholt Oliviero Stock Toyoaki Nishida

This Special Issue of AI and Society contains a selection of papers presented at the 6th Social Intelligence Design Workshop held at ITC-irst, Povo (Trento, Italy) in July 2007. Being the 6th in a series means that there now is a well-established and also a growing research area. The interest in this research area is growing because, among other things, current computing technology allows other than the traditional efficiency-oriented applications associated with computer science and interface technology. For example, in Ambient Intelligence (AmI) applications we look at sensor-equipped environments and devices (robots, smart furniture, virtual humans and pets) that support their human inhabitants during their everyday activities. These everyday activities also include computer-mediated communication, collaboration and community activities.

Ambient Intelligence

Social intelligence

Performing arts

10.1007/s00146-009-0192-7

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Citations (12)

Constructing a Weakly Structured Information Base with a Knowledge Medium

IEICE technical report. Artificial intelligence and knowledge-based processing (1995)

Kazuto Koujitani Harumi Maeda Toyoaki Nishida

Base (topology)

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Social intelligence design for mediated communication

AI & Society (2005)

Anton Nijholt Toyoaki Nishida

Performing arts

10.1007/s00146-005-0010-9

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Citations (6)

Eye gaze tracking using corneal imaging and active illumination devices

Advanced Robotics (2017)

Atsushi Nakazawa Hiroaki Kato Christian Nitschke Toyoaki Nishida

Graphical Abstract

Tracking (education)

10.1080/01691864.2016.1277552

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Citations (7)

TWO LAYERS ACTION INTEGRATION FOR HRI - Action Integration with Attention Focusing for Interactive Robots

Yasser Mohammad Toyoaki Nishida

Behavior architectures are widely used to program interactive robots. In these architectures multiple behaviors are usually running concurrently so a mechanism for integrating the resulting actuation commands from these behaviors into actual actuation commands sent to the robot’s motor system must be faced. Different architectures proposed different action integration mechanisms that range from distributed to central integration. In this paper an analysis of the special requirements that HRI imposes on the action integration system is given. Based on this analysis a novelle hybrid action integration mechanism that combines distributed attention focusing with a fast central integration algorithm is presented in the framework of the EICA architecture. The proposed system was tested in a simulation of a listener robot that aimed to achieve human-like nonverbal listening behavior in real world interactions. The analysis of the system showed that the proposed mechanism can generate coherent human-like behavior while being robust against signal correlated noise.

10.5220/0001482600410048

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Effect of back and mutual imitation on human's perception of a humanoid's imitative skill

Yasser Mohammad Toyoaki Nishida

A promising technology for teaching robots new skills is learning from demonstrations (LfD) or imitation learning. Even though there is currently large literature in imitation learning, smaller attention was given to studying social aspects of the imitation situation. Back imitation is the situation when the demonstrator (human) imitates the learner (robot) before engaging in the actual teaching. In this paper we study the effect of back-imitation on human's perception of the robot's imitative skill. This study is motivated by the need to improve the acceptance of the robot through increasing its perceived imitation skill. Our claim is that back-imitation can be used to achieve this goal. The paper provides two studies involving 24 subjects and 36 HRI sessions that were conducted to investigate this claim. Statistical analysis of the results of these studies show that participants assigned the robots with which they engaged in back-imitation higher scores in terms of imitative skill and overall performance compared with robots that they did not imitate. The paper discusses the implications of this result for the design of imitation situations and broader implications for HRI research.

Cognitive imitation

Human–robot interaction

10.1109/roman.2014.6926349

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Theoretical Framework for Interaction Game Design

Toyoaki Nishida

Abstract : The PI aimed at establishing a theoretical framework for building a common ground that may allow for content-rich, proficient and reliable communication between people and robots. To solicit high quality and spontaneous contribution from people, the PI needed to make the process of common ground acquisition as playful as possible, by incorporating aspects of game, story and play. The notion of interaction game is critical to induce a strong engagement of participants. This was addressed through learning from demonstration for acquiring interaction patterns, cognitive aspects of interaction game and an integrated framework for interaction game design. Major results encompass a robust learning algorithm from demonstration SAXImitate, an integrated toolbox MC2 (Motif Change and Causality Discovery), implementation and evaluation of a virtual basketball game, investigation of the effect of back imitation, a method for inducing intentional stance in HAI (Human-Agent Interaction), using physiological indices for discriminating intrinsic and extrinsic stress, and SES (Synthetic Evidential Study). Project had to be cancelled a year early due to political complications. Prof Nishida collaborated with Il Hong Suh, who is also funded by AOARD under FA2386-14-1-0009.

Common ground

Toolbox

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Learning interaction protocols by mimicking understanding and reproducing human interactive behavior

Pattern Recognition Letters (2014)

Yasser Mohammad Toyoaki Nishida

Human interaction

Human–robot interaction

10.1016/j.patrec.2014.11.010

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