Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2020

Abstract

Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale up to solve problems with a large number of participants due to the huge state space. Our contributions in this paper are fourfold. Firstly, we propose a new model that addresses the sequential intervention of participants while considering the propagation of emotions and formulate it as a Partially Observable Markov Decision Process (POMDP) to handle uncertainties about their mental states and the influence between them. Secondly, we apply reasoning to refine belief to improve solution quality for the lack of initial information on mental state values. Thirdly, we improve the scalability by the abstraction of states to reduce the number of states by representing the mental states with an abstracted discrete set. We further improve the scalability by multi-level partitioning to get smaller POMDPs. Finally, we conduct extensive experiments on both synthetic and real networks to show that our algorithm significantly improves scalability with comparable solution quality compared to the state-of-the-art algorithms.

Discipline

Applied Behavior Analysis | Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 30th International Conference on Automated Planning and Scheduling ICAPS 2020: Nancy, October 26-30

Volume

30

First Page

499

Last Page

507

Identifier

10.1609/icaps.v30i1.6745

Publisher

AAAI Press

City or Country

Palo Alto

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1609/icaps.v30i1.6745

Download

Share

COinS