Abstract
Our goal is to create a user-adaptive communication-robot. We are developing a system for evaluating human-robot interactions. Although such evaluation is indispensable for learning algorithms, users' preferences are too difficult to model because they are subjective. In this study, we used the interactive evolutionary computation (IEC) to configure the value system of a learning communicationrobot. The IEC is a genetic algorithm whose fitness function is performed by the user. In our experiment, we encoded the values of sensors (reward or punishment) into genes, and subjects interacted with the learning robot. Through the interaction, the subjects evaluated the robot by touching its sensors, and the robot learned appropriate combinations between input and output. Afterward, the subjects gave their scores to the experimenter, and the scores were regarded as the fitness values of the corresponding genes. These sequences were continued until the 4 generation, and then the subjects compared three of their best genes and two of the experimenter's. We found that the user-adaptive value system is suitable for the communication-robot.
| Original language | English |
|---|---|
| Title of host publication | IFAC Proceedings Volumes (IFAC-PapersOnline) |
| Volume | 17 |
| Edition | 1 PART 1 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul Duration: 2008 Jul 6 → 2008 Jul 11 |
Other
| Other | 17th World Congress, International Federation of Automatic Control, IFAC |
|---|---|
| City | Seoul |
| Period | 08/7/6 → 08/7/11 |
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Keywords
- Intelligent robotics
- Multi-modal interaction
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Development of user-adaptive value system of learning function using interactive EC. / Suga, Yuki; Sugano, Shigeki; Ikuma, Yoshinori; Ogata, Tetsuya.
IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Development of user-adaptive value system of learning function using interactive EC
AU - Suga, Yuki
AU - Sugano, Shigeki
AU - Ikuma, Yoshinori
AU - Ogata, Tetsuya
PY - 2008
Y1 - 2008
N2 - Our goal is to create a user-adaptive communication-robot. We are developing a system for evaluating human-robot interactions. Although such evaluation is indispensable for learning algorithms, users' preferences are too difficult to model because they are subjective. In this study, we used the interactive evolutionary computation (IEC) to configure the value system of a learning communicationrobot. The IEC is a genetic algorithm whose fitness function is performed by the user. In our experiment, we encoded the values of sensors (reward or punishment) into genes, and subjects interacted with the learning robot. Through the interaction, the subjects evaluated the robot by touching its sensors, and the robot learned appropriate combinations between input and output. Afterward, the subjects gave their scores to the experimenter, and the scores were regarded as the fitness values of the corresponding genes. These sequences were continued until the 4 generation, and then the subjects compared three of their best genes and two of the experimenter's. We found that the user-adaptive value system is suitable for the communication-robot.
AB - Our goal is to create a user-adaptive communication-robot. We are developing a system for evaluating human-robot interactions. Although such evaluation is indispensable for learning algorithms, users' preferences are too difficult to model because they are subjective. In this study, we used the interactive evolutionary computation (IEC) to configure the value system of a learning communicationrobot. The IEC is a genetic algorithm whose fitness function is performed by the user. In our experiment, we encoded the values of sensors (reward or punishment) into genes, and subjects interacted with the learning robot. Through the interaction, the subjects evaluated the robot by touching its sensors, and the robot learned appropriate combinations between input and output. Afterward, the subjects gave their scores to the experimenter, and the scores were regarded as the fitness values of the corresponding genes. These sequences were continued until the 4 generation, and then the subjects compared three of their best genes and two of the experimenter's. We found that the user-adaptive value system is suitable for the communication-robot.
KW - Intelligent robotics
KW - Multi-modal interaction
UR - http://www.scopus.com/inward/record.url?scp=79961019181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961019181&partnerID=8YFLogxK
U2 - 10.3182/20080706-5-KR-1001.3287
DO - 10.3182/20080706-5-KR-1001.3287
M3 - Conference contribution
AN - SCOPUS:79961019181
SN - 9783902661005
VL - 17
BT - IFAC Proceedings Volumes (IFAC-PapersOnline)
ER -