Peter Stone's Selected Publications

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Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies.
Roberto Capobianco, Varun Kompella, James Ault, Guni Sharon, Stacy Jong, Spencer Fox, Lauren Meyers, Peter R. Wurman, and Peter Stone.
The Journal of Artificial Intelligence Research (JAIR), 71:953–92, August 2021.
Contains material that was previously published in an AAMAS 2021 paper and a AAAI 2020 Fall Symposium paper.
Also available from JAIR website.
Simulator source code.

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Abstract

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts.

BibTeX Entry

@article{JAIR21-covid,
  author = {Roberto Capobianco and Varun Kompella and James Ault and Guni Sharon and Stacy Jong and Spencer Fox and Lauren Meyers and Peter R.\ Wurman and Peter Stone},
  title = {Agent-Based Markov Modeling for Improved {COVID}-19 Mitigation Policies},
  journal={The Journal of Artificial Intelligence Research (JAIR)},
  volume={71},
  year = {2021},
  pages={953--92},
  month={August},
  abstract = {
              The year 2020 saw the covid-19 virus lead to one of the
              worst global pandemics in history. As a result,
              governments around the world have been faced with the
              challenge of protecting public health while keeping the
              economy running to the greatest extent
              possible. Epidemiological models provide insight into
              the spread of these types of diseases and predict the
              effects of possible intervention policies. However, to
              date, even the most data-driven intervention policies
              rely on heuristics. In this paper, we study how
              reinforcement learning (RL) and Bayesian inference can
              be used to optimize mitigation policies that minimize
              economic impact without overwhelming hospital
              capacity. Our main contributions are (1) a novel
              agent-based pandemic simulator which, unlike traditional
              models, is able to model fine-grained interactions among
              people at specific locations in a community; (2) an
              RLbased methodology for optimizing fine-grained
              mitigation policies within this simulator; and (3) a
              Hidden Markov Model for predicting infected individuals
              based on partial observations regarding test results,
              presence of symptoms, and past physical contacts.
  },
Also available from <a href="https://www.jair.org/index.php/jair/article/view/12632"> JAIR website</a>.<br>
Simulator <a href="https://github.com/SonyAI/PandemicSimulator">source code</a>.},
}

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