* 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

Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

Nature, 2022
Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, Hao Chih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead Amago, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block…

Planetary Environment Prediction Using Generative Modeling

AIAA SciTech Forum, 2022
Shrijit Singh*, Shreyansh Daftry*, Roberto Capobianco

Planetary rovers have a limited sensory horizon and operate in environments where limited information about the surrounding terrain is available. The rough and unknown nature of the terrain in planetary environments potentially leads to scenarios where the rover gets stuckan…

Tafl-ES: Exploring Evolution Strategies for Asymmetrical Board Games

AIxIA, 2021
Roberto Gallotta*, Roberto Capobianco

NeuroEvolution Strategies (NES) are a subclass of Evolution Strategies (ES). While their application to games and board games have been studied in the past [11], current state of the art in most of the games is still held by classic RL models, such as AlphaGo Zero [16]. This…

  • 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.