* External authors




Multiagent Epidemiologic Inference through Realtime Contact Tracing

Guni Sharon*

James Ault*

Peter Stone

Varun Kompella

Roberto Capobianco

* External authors




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

Benchmarking Reinforcement Learning Techniques for Autonomous Navigation

ICRA, 2023
Zifan Xu*, Bo Liu*, Xuesu Xiao*, Anirudh Nair*, Peter Stone

Deep reinforcement learning (RL) has broughtmany successes for autonomous robot navigation. However,there still exists important limitations that prevent real-worlduse of RL-based navigation systems. For example, most learningapproaches lack safety guarantees; and learned na…

Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

ICRA, 2023
Jin-Soo Park*, Xuesu Xiao*, Garrett Warnell*, Harel Yedidsion*, Peter Stone

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other i…

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Neural Networks, 2023
Megan M. Baker*, Alexander New*, Mario Aguilar-Simon*, Ziad Al-Halah*, Sébastien M. R. Arnold*, Ese Ben-Iwhiwhu*, Andrew P. Brna*, Ethan Brooks*, Ryan C. Brown*, Zachary Daniels*, Anurag Daram*, Fabien Delattre*, Ryan Dellana*, Eric Eaton*, Haotian Fu*, Kristen Grauman*, Jesse Hostetler*, Shariq Iqbal*, Cassandra Kent*, Nicholas Ketz*, Soheil Kolouri*, George Konidaris*, Dhireesha Kudithipudi*, Seungwon Lee*, Michael L. Littman*, Sandeep Madireddy*, Jorge A. Mendez*, Eric Q. Nguyen*, Christine D. Piatko*, Praveen K. Pilly*, Aswin Raghavan*, Abrar Rahman*, Santhosh Kumar Ramakrishnan*, Neale Ratzlaff*, Andrea Soltoggio*, Peter Stone, Indranil Sur*, Zhipeng Tang*, Saket Tiwari*, Kyle Vedder*, Felix Wang*, Zifan Xu*, Angel Yanguas-Gil*, Harel Yedidsion*, Shangqun Yu*, Gautam K. Vallabha*

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and syst…

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


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.