Abstract
How can we generate appropriate behavior for social artificial agents? A common approach is to (1) establish with controlled experiments which action is most appropriate in which setting, and (2) select actions based on this knowledge and an estimate of the setting. This approach faces challenges, as it can be very hard to acquire and reason with all the required knowledge. Estimating the setting is challenging too, as many relevant aspects of the setting (e.g. personality of the interactee) can be unobservable. We formally describe an alternative approach that can handle these challenges; responsiveness. This is the idea that a social agent can utilize the many feedback cues given in social interactions to continuously adapt its behavior to something more appropriate. We theoretically discuss the relative advantages and disadvantages of these two approaches, which allows for more explicitly considering their application in social agents.
Original language | English |
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Title of host publication | Social Robotics |
Subtitle of host publication | 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings |
Editors | Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, Hongsheng He |
Pages | 126-137 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-319-47437-3 |
DOIs | |
Publication status | Published - Nov 2016 |
Event | 8th International Conference on Social Robotics, ICSR 2016 - Kansas City, United States Duration: 1 Nov 2016 → 3 Nov 2016 Conference number: 8 |
Publication series
Name | Lecture notes in computer science |
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Volume | 9979 |
Conference
Conference | 8th International Conference on Social Robotics, ICSR 2016 |
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Abbreviated title | ICSR 2016 |
Country/Territory | United States |
City | Kansas City |
Period | 1/11/16 → 3/11/16 |
Keywords
- Feedback
- Control architectures
- IR-104076
- Social Robotics
- EWI-27601