Safety first: Strategic navigation in adversarial enviornments

Ofri Keidar, Noa Agmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This work deals with the problem of navigation while avoiding detection by a mobile adversary, which is a novel variant of pursuit-evasion featuring adversarial modeling. In this problem, an evading agent is placed on a graph, where one or more nodes are defined as safehouses. The agent's goal is to find a path from its current location to a safe-house, while minimizing the probability of meeting a mobile adversarial agent at a node along its path (i.e., being captured). We examine several models of this problem, where each one has different assumptions on what the agents know about their opponent, all using a framework for computing node utility, introduced herein. We use several risk attitudes for computing the utility values, whose impact on the constructed strategies is analyzed both theoretically and empirically. Furthermore, we allow the agents to use information gained along their movement, in order to efficiently update their motion strategies on-the-fly. Analytic and empirical analysis show the importance of using this information and these on-the-fly strategy updates.

Original languageEnglish
Title of host publication16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
EditorsEdmund Durfee, Michael Winikoff, Kate Larson, Sanmay Das
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1581-1583
Number of pages3
ISBN (Electronic)9781510855076
StatePublished - 2017
Event16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil
Duration: 8 May 201712 May 2017

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
Country/TerritoryBrazil
CitySao Paulo
Period8/05/1712/05/17

Bibliographical note

Publisher Copyright:
© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Keywords

  • Adversarial modeling
  • Pursuit-evasion
  • Robot navigation

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