Name
Abstract | ||
|---|---|---|
Social capability of a robot becomes one of important issues in human-robot interaction (HRI). Especially, for a robot to form a social relationship with people is significant for improving believability of a robot through more natural communication with people. In this paper, we propose a formal approach to make a robot establish and learn a social relationship based on affective relation in sociological perspectives. The main idea is based on representing sentiment relation (liking/disliking) within social individuals, which is regarded as a basis for forming interpersonal relation in sociology. Our Sentiment Relation model can be applied to loyalty implementation of service robots in underlying assumptions that a service robot must have high positive relationship to her host and tends to behave to minimize tension(stress) by unbalanced states, which are generated by different affective states between individuals in social group. To confirm the possibility of our model, the reinforcement learning-based behavior coordination using loyalty level is simulated in the simple grid world. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ROMAN.2007.4415234 | RO-MAN |
Keywords | Field | DocType |
reinforcement learning,man-machine systems,interpersonal relation,behavior coordination,learning (artificial intelligence),human-robot interaction,reinforcement learning-based behavior coordination,sociology,service robots,interactive systems,sentiment relation model,social relationship,loyalty,balance theory,social interaction,interpersonal relations,social groups,relational model,learning artificial intelligence,human robot interaction | Balance theory,Social group,Simulation,Computer science,Interpersonal relationship,Loyalty,Robot,Affect (psychology),Reinforcement learning,Service robot | Conference |
ISBN | Citations | PageRank |
978-1-4244-1635-6 | 1 | 0.42 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Young-Min Kim | 1 | 442 | 46.66 |
| Jong-chan Park | 2 | 14 | 1.93 |
| Dong-Soo Kwon | 3 | 753 | 119.20 |