To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability

Authors

  • Elizabeth Bondi Harvard University
  • Hoon Oh Carnegie Mellon University
  • Haifeng Xu University of Virginia
  • Fei Fang Carnegie Mellon University
  • Bistra Dilkina University of Southern California
  • Milind Tambe Harvard University

DOI:

https://doi.org/10.1609/aaai.v34i02.5493

Abstract

Motivated by real-world deployment of drones for conservation, this paper advances the state-of-the-art in security games with signaling. The well-known defender-attacker security games framework can help in planning for such strategic deployments of sensors and human patrollers, and warning signals to ward off adversaries. However, we show that defenders can suffer significant losses when ignoring real-world uncertainties despite carefully planned security game strategies with signaling. In fact, defenders may perform worse than forgoing drones completely in this case. We address this shortcoming by proposing a novel game model that integrates signaling and sensor uncertainty; perhaps surprisingly, we show that defenders can still perform well via a signaling strategy that exploits uncertain real-time information. For example, even in the presence of uncertainty, the defender still has an informational advantage in knowing that she has or has not actually detected the attacker; and she can design a signaling scheme to “mislead” the attacker who is uncertain as to whether he has been detected. We provide theoretical results, a novel algorithm, scale-up techniques, and experimental results from simulation based on our ongoing deployment of a conservation drone system in South Africa.

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Published

2020-04-03

How to Cite

Bondi, E., Oh, H., Xu, H., Fang, F., Dilkina, B., & Tambe, M. (2020). To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 1369-1377. https://doi.org/10.1609/aaai.v34i02.5493

Issue

Section

AAAI Technical Track: Computational Sustainability