Authors

* External authors

Venue

Date

Share

Multiagent Epidemiologic Inference through Realtime Contact Tracing

Guni Sharon*

James Ault*

Peter Stone

Varun Kompella

Roberto Capobianco

* External authors

AAMAS-2021

2021

Abstract

This paper addresses an epidemiologic inference problem where, given realtime observation of test results, presence of symptoms,
and physical contacts, the most likely infected individuals need to be inferred. The inference problem is modeled as a hidden Markov
model where infection probabilities are updated at every time step and evolve between time steps. We suggest a unique inference
approach that avoids storing the given observations explicitly. Theoretical justification for the proposed model is provided under specific simplifying assumptions. To complement these theoretical results, a comprehensive experimental study is performed using a custom-built agent-based simulator that models inter-agent contacts. The reported results show the effectiveness of the proposed
inference model when considering more realistic scenarios – where the simplifying assumptions do not hold. When pairing the proposed inference model with a simple testing and quarantine policy, promising trends are obtained where the epidemic progression is significantly slowed down while quarantining a bounded number of individuals.

Related Publications

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

NeurIPS, 2022
Bo Liu*, Mao Ye*, Stephen Wright*, Peter Stone, Qiang Liu*

Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning. Conventional BO methods need to differentiate through the…

Value Function Decomposition for Iterative Design of Reinforcement Learning Agents

NeurIPS, 2022
James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone

Designing reinforcement learning (RL) agents is typically a difficult process that requires numerous design iterations. Learning can fail for a multitude of reasons and standard RL methods provide too few tools to provide insight into the exact cause. In this paper, we show …

Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System

IROS, 2022
Keya Ghonasgi*, Reuth Mirsky*, Adrian M Haith*, Peter Stone, Ashish D Deshpande*

While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics t…

  • HOME
  • Publications
  • Multiagent Epidemiologic Inference through Realtime Contact Tracing

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.