TY - GEN
T1 - The Effects of Cognitive Biases in Long-Term Human-Robot Interactions
T2 - 8th International Conference on Social Robotics, ICSR 2016
AU - Biswas, Mriganka
AU - Murray, John
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016/10/7
Y1 - 2016/10/7
N2 - The research presented in this paper is part of a wider study investigating the role cognitive bias plays in developing long-term companionship between a robot and human. In this paper we discuss, how cognitive biases such as misattribution, Empathy gap and Dunning-Kruger effects can play a role in robot-human interaction with the aim of improving long-term companionship. One of the robots used in this study called MARC (See Fig. 1) was given a series of biased behaviours such as forgetting participant’s names, denying its own faults for failures, unable to understand what a participant is saying, etc. Such fallible behaviours were compared to a non-biased baseline behaviour. In the current paper, we present a comparison of two case studies using these biases and a non-biased algorithm. It is hoped that such humanlike fallible characteristics can help in developing a more natural and believable companionship between Robots and Humans. The results of the current experiments show that the participants initially warmed to the robot with the biased behaviours.
AB - The research presented in this paper is part of a wider study investigating the role cognitive bias plays in developing long-term companionship between a robot and human. In this paper we discuss, how cognitive biases such as misattribution, Empathy gap and Dunning-Kruger effects can play a role in robot-human interaction with the aim of improving long-term companionship. One of the robots used in this study called MARC (See Fig. 1) was given a series of biased behaviours such as forgetting participant’s names, denying its own faults for failures, unable to understand what a participant is saying, etc. Such fallible behaviours were compared to a non-biased baseline behaviour. In the current paper, we present a comparison of two case studies using these biases and a non-biased algorithm. It is hoped that such humanlike fallible characteristics can help in developing a more natural and believable companionship between Robots and Humans. The results of the current experiments show that the participants initially warmed to the robot with the biased behaviours.
KW - Cognitive bias in robot
KW - Human-robot interaction
KW - Human-robot long-term interactions
KW - Imperfect robot
UR - https://www.scopus.com/pages/publications/84992530101
UR - https://link.springer.com/book/10.1007/978-3-319-47437-3
U2 - 10.1007/978-3-319-47437-3_15
DO - 10.1007/978-3-319-47437-3_15
M3 - Conference contribution
AN - SCOPUS:84992530101
SN - 9783319474366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 148
EP - 158
BT - Social Robotics
A2 - Agah, Arvin
A2 - Cabibihan, John-John
A2 - Howard, Ayanna M.
A2 - Salichs, Miguel A.
A2 - He, Hongsheng
PB - Springer, Cham
Y2 - 1 November 2016 through 3 November 2016
ER -