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Dynamic optimization of the strength ratio during a terrestrial conflict

Authors :
O. Hudry
G. Coppin
A. Sztykgold
Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)
Traitement Algorithmique et Matériel de la Communication, de l'Information et de la Connaissance (TAMCIC)
Ecole Nationale Supérieure des Télécommunications de Bretagne-Centre National de la Recherche Scientifique (CNRS)
Laboratoire Traitement et Communication de l'Information (LTCI)
Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)
Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB)
Source :
Proceedings ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Apr 2007, Honolulu, United States. pp.241-246, ⟨10.1109/ADPRL.2007.368194⟩
Publication Year :
2007
Publisher :
HAL CCSD, 2007.

Abstract

International audience; The aim of this study is to assist a military decision maker during his decision-making process when applying tactics on the battlefield. For that, we have decided to model the conflict by a game, on which we will seek to find strategies guaranteeing to achieve given goals simultaneously defined in terms of attrition and tracking. The model relies multi-valued graphs, and leads us to solve a stochastic shortest path problem. The employed techniques refer to temporal differences methods but also use a heuristic qualification of system states to face algorithmic complexity issues

Details

Language :
English
Database :
OpenAIRE
Journal :
Proceedings ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007 : IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Apr 2007, Honolulu, United States. pp.241-246, ⟨10.1109/ADPRL.2007.368194⟩
Accession number :
edsair.doi.dedup.....e7176f5785bf2fbbdc504bf6a00621b9