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Dynamic optimization of the strength ratio during a terrestrial conflict
- 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
- Subjects :
- Mathematical optimization
Computational complexity theory
Computer science
Process (engineering)
Heuristic
Decision theory
Graph theory
[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
010501 environmental sciences
Dynamic programming
01 natural sciences
Computational complexity
03 medical and health sciences
0302 clinical medicine
Shortest path problem
030221 ophthalmology & optometry
Game theory
Decision making
0105 earth and related environmental sciences
Subjects
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