# MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling

## TL;DR

Meeting Bot employs reinforcement learning to create an adaptive conversational system that efficiently schedules meetings by understanding user preferences and adjusting strategies in real-time.

## Contribution

The paper introduces a novel RL-based dialogue system for meeting scheduling that learns and adapts to user preferences and environmental changes.

## Key findings

- Outperforms standard scheduling algorithms in efficiency
- Adapts to user preferences and environmental changes
- Uses policy gradient with exploration for learning

## Abstract

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.