2018/12/28 by Dhanraj Vishwanath, Vishwanath D, Lovekesh Vig +6 · 1 citation
Computer Science · Decision Sciences · #AI in Service Interactions #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Cognitive Radio Networks and Spectrum Sensing #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Speech and dialogue systems #Topic Modeling #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1812.11158
arxiv created 2018/12/28 · openalex publication_date 2018/12/28 · arxiv updated 2018/12/31 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
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.