Abstract
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 language | English |
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Title of host publication | Frontiers in Artificial Intelligence and Applications |
Editors | Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hullermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen |
Publisher | IOS Press BV |
Pages | 1579-1580 |
Number of pages | 2 |
ISBN (Electronic) | 9781614996712 |
DOIs | |
State | Published - 2016 |
Event | 22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands Duration: 29 Aug 2016 → 2 Sep 2016 |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Volume | 285 |
ISSN (Print) | 0922-6389 |
ISSN (Electronic) | 1879-8314 |
Conference
Conference | 22nd European Conference on Artificial Intelligence, ECAI 2016 |
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Country/Territory | Netherlands |
City | The Hague |
Period | 29/08/16 → 2/09/16 |
Bibliographical note
Publisher Copyright:© 2016 The Authors and IOS Press.