Abstract:
Robots are becoming a part of the human society.
They are intended to maintain long-term social interactions with
humans. A memory about each interaction partner is essential for
a robot for maintaining long-term social interactions. Therefore,
robot memory must possess the abilities for extracting, storing,
updating and recalling information during social interactions.
This work studies the application of a robot’s autobiographical
memory in long-term social interactions in multi-user environments.
The proposed system is capable of acquiring knowledge
about users through friendly conversations and recall them
during future interactions. The autobiographical memory which
has been designed with a three-layered architecture is employed
for storing significant information which is required for long-term
social interactions. The interaction manager module has been
designed as a finite state machine for managing the interaction
between the robot and the user. Dialogue flows were defined for
each state to communicate with the user. The system has been
tested and validated by using a set of experiments.
Citation:
Edirisinghe, M.M.S.N., Muthugala, M.A.V.J., & Jayasekara, A.G.B.P. (2018). Application of robot autobiographical memory in long-term human-robot social interactions. In R. Samarasinghe & S. Abeygunawardana (Eds.), Proceedings of 2nd International Conference on Electrical Engineering 2018 (pp. 138-143). Institute of Electrical and Electronics Engineers, Inc. https://ieeexplore.ieee.org/xpl/conhome/8528200/proceeding