Strategic path planning allowing on-the-fly updates

Ofri Keidar, Noa Agmon

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


This work deals with the problem of strategic path planning while avoiding detection by a mobile adversary. 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 safehouse, 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. We use several risk attitudes for computing the utility values, whose impact on the actual performance of the path planning algorithms is highlighted by an empirical analysis. 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 empiric analysis show that on-the-fly updates increase the probability that our agent reaches its destination safely.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
EditorsGal A. Kaminka, Frank Dignum, Eyke Hullermeier, Paolo Bouquet, Virginia Dignum, Maria Fox, Frank van Harmelen
PublisherIOS Press
Number of pages2
ISBN (Electronic)9781614996712
StatePublished - 2016
Event22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands
Duration: 29 Aug 20162 Sep 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications


Conference22nd European Conference on Artificial Intelligence, ECAI 2016
CityThe Hague

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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