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In this paper we present Meeting Bot, a reinforcement learning basedconversational system that interacts with multiple users to schedule meetings.The system is able to interpret user utterences and map them to preferred timeslots, which are then fed to a reinforcement learning (RL) system with the goalof converging on an agreeable time slot. The RL system is able to adapt to userpreferences and environmental changes in meeting arrival rate while stillscheduling effectively. Learning is performed via policy gradient withexploration, by utilizing an MLP as an approximator of the policy function.Results demonstrate that the system outperforms standard scheduling algorithmsin terms of overall scheduling efficiency. Additionally, the system is able toadapt its strategy to situations when users consistently reject or acceptmeetings 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.

MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling