Multi-Agent Systems Approaches for COVID-19 and Future Pandemics: Challenges and Opportunities
Virtual Tutorial Presented at AAMAS 2021, London, United Kingdom
Date: Monday, 4th May, 2021
Time: 10 AM - 1 PM EST (New York Time)
Venue: Zoom (Links to be given to attendees over Underline)
COVID-19 (or coronavirus) is the greatest public health crisis that the world has experienced in the last century. Tackling it effectively requires the collective will of experts from a variety of disciplines. From a computer science perspective, Artificial Intelligence (and Multi-Agent Systems) researchers have traditionally been at the forefront of responding to rapidly emerging societal needs. The proposed tutorial will engage attendees in meaningful discussions around the different ways in which Multi-Agent Systems (MAS) research can help in tackling the COVID-19 pandemic. Clearly, MAS researchers have already made rapid strides in this space For example, a lot of efforts have been made by MAS researchers in developing agent-based models for simulating the transmission of COVID-19. In addition, MAS researchers have proposed Markovian models for generated optimal sequential intervention strategies for effective imposition and release of lockdown protocols. At the same time, we have not yet seen the enormous potential of MAS research being leveraged to design decision support systems (e.g., in the allocation of limited healthcare resources such as testing kits) which can assist epidemiologists and policy makers in their fight against this pandemic.
In this regard, this half-day tutorial has two aims. First, it is aimed at providing an expansive overview of different areas of COVID-19 research and policy planning which can be positively impacted by Multi-Agent Systems research. Second, it will cover the current state-of-the-art, i.e., it will include a methodical and guided literature review of Multi-Agent Systems (and AI) research on COVID-19. Finally, and most importantly, the third part of the tutorial will propose topics of research which need urgent investment of time (by MAS researchers) in order to get the greatest benefits. In particular, the tutorial will point out tangible research questions that would arise from an interdisciplinary approach to COVID-19, and propose possible solutions.
This tutorial is geared toward undergraduate and graduate students, AI researchers, and practitioners, who are interested in COVID-19, and want to learn about how to utilize their MAS skills and techniques to tackle this important crisis facing humanity. The tutorial will be made interactive, and attendees will be encouraged to provide their own ideas about MAS research for COVID-19.
While there exist many different areas in which AI can help in the fight against this pandemic, we propose to focus on the following five strands of MAS research for COVID-19:
The main topics of the tutorial will be:
-What is COVID-19?
-Characteristics of this pandemic which MAS research can exploit
Topics Fertile for MAS Research
MAS research for Transmission Modeling
MAS research for COVID-19 Forecasting
MAS research for Optimal Healthcare Resource Utilization
MAS research for Vaccine Discovery
MAS research for knowledge mining using CORD-19
Overview of Current MAS Research for COVID-19
- Possible Future Directions
Slides Deck 1: https://cutt.ly/PbbpFrR
Slides Deck 2: http://bit.ly/AAMAS21-COVID19-SV
|Amulya Yadav||Srini Venkaramanan|
Amulya Yadav is the PNC Technologies Career Development Assistant Professor in the College of Information Sciences and Technology at Penn State University. His research interests include Artificial Intelligence, Multi-Agent Systems, Computational Game-Theory and Applied Machine Learning.
Srini Venkatramanan is a research scientist at the Network Systems Science and Advanced Computing division at the Biocomplexity Institute in the University of Virginia. Venkatramanan’s research areas include stochastic modeling, diffusion dynamics, optimal control and network science. At the Biocomplexity Institute, he is responsible for developing, analyzing and optimizing computational models for complex systems arising in the domains of epidemiology and food security.