  In this paper we present Meeting Bot, a reinforcement learning based
conversational system that interacts with multiple users to schedule meetings.
The system is able to interpret user utterences and map them to preferred time
slots, which are then fed to a reinforcement learning (RL) system with the goal
of converging on an agreeable time slot. The RL system is able to adapt to user
preferences and environmental changes in meeting arrival rate while still
scheduling effectively. Learning is performed via policy gradient with
exploration, by utilizing an MLP as an approximator of the policy function.
Results demonstrate that the system outperforms standard scheduling algorithms
in terms of overall scheduling efficiency. Additionally, the system is able to
adapt its strategy to situations when users consistently reject or accept
meetings in certain slots (such as Friday afternoon versus Thursday morning),
or when the meeting is called by members who are at a more senior designation.
