TELKOMNIKA Telecommunication, Computing, Electronics and Control
Dialogue management using reinforcement learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Dialogue management using reinforcement learning

Dialogue management
Human-robot interaction
Knowledge growing
Reinforcement learning

Dialogue has been widely used for verbal communication between human
and robot interaction, such as assistant robot in hospital. However, this robot
was usually limited by predetermined dialogue, so it will be difficult to
understand new words for new desired goal. In this paper, we discussed
conversation in Indonesian on entertainment, motivation, emergency, and
helping with knowledge growing method. We provided mp3 audio for music,
fairy tale, comedy request, and motivation. The execution time for this
request was 3.74 ms on average. In emergency situation, patient able to ask
robot to call the nurse. Robot will record complaint of pain and inform nurse.
From 7 emergency reports, all complaints were successfully saved on
database. In helping conversation, robot will walk to pick up belongings of
patient. Once the robot did not understand with patient’s conversation, robot
will ask until it understands. From asking conversation, knowledge expands
from 2 to 10, with learning execution from 1405 ms to 3490 ms. SARSA was
faster towards steady state because of higher cumulative rewards. Q-learning
and SARSA were achieved desired object within 200 episodes. It concludes
that reinforcement learning (RL) method to overcome robot knowledge
limitation in achieving new dialogue goal for patient assistant were achieved.

Binashir Rofi’ah, Hanif Fakhrurroja, Carmadi Machbub

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 14, 2020

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