Reference : Multistage stochastic programming: A scenario tree based approach to planning under u...
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/80246
Multistage stochastic programming: A scenario tree based approach to planning under uncertainty
English
Defourny, Boris [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
Ernst, Damien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2011
Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions
Sucar, L. Enrique mailto
Morales, Eduardo F. mailto
Hoey, Jesse mailto
Information Science Publishing
978-1609601652
Hershey
Pennsylvania, USA
[en] Sequential decision making under uncertainty ; scenario tree generation ; validation of approximate solutions
[en] In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty. We discuss its differences with Markov Decision Processes, from the point of view of decision models and solution algorithms. We describe the standard technique for solving approximately multistage stochastic problems, which is based on a discretization of the disturbance space called scenario tree. We insist on a critical issue of the approach: the decisions can be very sensitive to the parameters of the scenario tree, whereas no efficient tool for checking the quality of approximate solutions exists. In this chapter, we show how supervised learning techniques can be used to evaluate reliably the quality of an approximation, and thus facilitate the selection of a good scenario tree. The framework and solution techniques presented in the chapter are explained and detailed on several examples. Along the way, we define notions from decision theory that can be used to quantify, for a particular problem, the advantage of switching to a more sophisticated decision model.
Systems and Modeling Research Unit
DYSCO (Dynamical Systems, Control, and Optimization); FRS-FNRS; PASCAL2 Network of Excellence
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/80246

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