Yoshimasa Ohmoto

G-SteX: greedy stem extension for free-length constrained motif discovery

Most available motif discovery algorithms in real-valued time series find approximately recurring patterns of a known length without any prior information about their locations or shapes. In this paper, a new motif discovery algorithm is proposed that has the advantage of requiring no upper limit on the motif length. The proposed algorithm can discover multiple motifs of multiple lengths at once, and can achieve a better accuracy-speed balance compared with a recently proposed motif discovery algorithm. We then briefly report two successful applications of the proposed algorithm to gesture discovery and robot motion pattern discovery.

A method to dynamically estimate emphasizing points and degree by using verbal and nonverbal information and physiological indices

The differences in the concepts of values which are the basis of human actions are important when we analyze intercultural communication. The purpose of this study is to estimate emphasizing points for decision-making, which is one of the concepts of values. For this purpose, we experimentally investigate how we could estimate discoveries new factors and increase degree of emphasis by using verbal information, nonverbal behavior and physiological indices. From the results of the investigation, we propose a method to dynamically estimate emphasizing points when two propositions were explained and a participant was asked what were his/her demands. We also conducted an experiment to evaluate our proposed method. As a result, we confirmed that the proposed method could accurately estimate the emphasizing points.

Real-time system for measuring gaze direction and facial features: towards automatic discrimination of lies using diverse nonverbal information

Interactive and autonomous agents might be common in everyday life in the future; we expect that such agents will have the ability to communicate with people naturally. For natural communication, the agents should speculate about the intentions of the people they interact with. To enable agents to speculate about intentions like deception, we focused on unconscious expressions when people tell a lie. However, there is no system that can meet the necessary conditions for measuring nonverbal information in natural communication. Therefore, we made a real-time system for measuring gaze direction and facial features. We conducted experiments for discriminating lies by using the system in a situation similar to actual communication. As a result, we found that we could discriminate lies by using diverse nonverbal information in the same way people did.

Formation conditions of mutual adaptation in human-agent collaborative interaction

When an adaptive agent works with a human user in a collaborative task, in order to enable flexible instructions to be issued by ordinary people, it is believed that a mutual adaptation phenomenon can enable the agent to handle flexible mapping relations between the human user’s instructions and the agent’s actions. To elucidate the conditions required to induce the mutual adaptation phenomenon, we designed an appropriate experimental environment called “WAITER” (Waiter Agent Interactive Training Experimental Restaurant) and conducted two experiments in this environment. The experimental results suggest that the proposed conditions can induce the mutual adaptation phenomenon.

ICIE: Immersive Environment for Social Interaction Based on Socio-spacial Information

Spacial information plays an important role in social interaction with people. The ICIE is a platform which can present socio-spacial information, obtain human behavior with non-contact sensors and have components to interpret the socio-spacial information. In this paper, we explain the framework of ICIE and main architectures to capture human behavior and to provide virtual space in ICIE. We discuss socio-spacial interaction by using ICIE and introduce some applications and studies using ICIE.

Capture and express behavior environment (CEBE) for realizing enculturating human-agent interaction

We are studying how Embodied Conversational Agents (ECAs) express communication behavior with cultural background. The objective of this study is the proposition of the modified Capture and Express Behavior Environment (CEBE) in which a person can interact with ECAs controlled by the captured behavior of another person with cultural background. In this paper, we discuss modifications and concepts of CEBE to apply CEBE for investigations to realize an ECA with cultural background. The prototype system could capture basic human behavior, such as head direction, posture of the upper body, and 3D angles of arms, when each part of the body, such as head, hands, arms and trunk. In addition, the system could control a robot or a virtual agent based on the detected data. We have to develop some implementations to interact with people with cultural background.

Synthetic Evidential Study as Primordial Soup of Conversation

Synthetic evidential study (SES for short) is a novel technology-enhanced methodology for combining theatrical role play and group discussion to help people spin stories by bringing together partial thoughts and evidences. SES not only serves as a methodology for authoring stories and games but also exploits the framework of game framework to help people sustain in-depth learning. In this paper, we present the conceptual framework of SES, a computational platform that supports the SES workshops, and advanced technologies for increasing the utility of SES. The SES is currently under development. We discuss conceptual issues and technical details to delineate how much we can implement the idea with our technology and how much challenges are left for the future work.

Synthetic Evidential Study as Augmented Collective Thought Process – Preliminary Report

Synthetic evidential study (SES) is a novel approach to understanding and augmenting collective thought process through substantiation by interactive media. It consists of a role-play game by participants, projecting the resulting play into a shared virtual space, critical discussions with mediated role-play, and componentization for reuse. We present the conceptual framework of SES, initial findings from a SES workshop, supporting technologies for SES, potential applications of SES, and future challenges.

Detection of Hidden Laughter for Human-agent Interaction☆

Abstract Our goal is to make a system to detect the times at which one almost laughed but he or she did not show their laughter on his/her face. We define this kind of laughter as hidden laughter. To accomplish this goal, we first tried making decision trees to detect ones amusement, the input data of which were physiological indices. We used 10-fold cross validation to evaluate the trees, and their accuracy was more than 70%. In addition, we investigated the effect of cultural background on the accuracy.

A Platform System for Developing a Collaborative Mutually Adaptive Agent

The characteristic task of service robots that can interact with humans is to achieve human-robot collaboration. Mutual adaptation is considered to be an important characteristic of robots, required for carrying out such collaborative tasks. Here, we introduce the concept of mutual adaptation, propose a learning model, and describe an experimental task to explain the above concept. A waiter robot performs a collaborative task using a platform system, which is developed by a constructive approach. The interactive and manual modes of this system are compared by performing a preliminary experiment to evaluate the effectiveness of the robots autonomous function. The results indicate that the robots autonomous function works well when operated in the interactive mode under short time or slow speed conditions.