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AAAI
2007
13 years 10 months ago
Purely Epistemic Markov Decision Processes
Planning under uncertainty involves two distinct sources of uncertainty: uncertainty about the effects of actions and uncertainty about the current state of the world. The most wi...
Régis Sabbadin, Jérôme Lang, N...
CCE
2008
13 years 7 months ago
Chance constrained programming approach to process optimization under uncertainty
Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging tas...
Pu Li, Harvey Arellano-Garcia, Günter Wozny
VLDB
2007
ACM
169views Database» more  VLDB 2007»
14 years 8 months ago
Staying FIT: Efficient Load Shedding Techniques for Distributed Stream Processing
In distributed stream processing environments, large numbers of continuous queries are distributed onto multiple servers. When one or more of these servers become overloaded due t...
Nesime Tatbul, Stanley B. Zdonik, Ugur Çeti...
ATAL
2005
Springer
14 years 1 months ago
A polynomial algorithm for decentralized Markov decision processes with temporal constraints
One of the difficulties to adapt MDPs for the control of cooperative multi-agent systems, is the complexity issued from Decentralized MDPs. Moreover, existing approaches can not ...
Aurélie Beynier, Abdel-Illah Mouaddib
ECAI
2010
Springer
13 years 8 months ago
On Finding Compromise Solutions in Multiobjective Markov Decision Processes
A Markov Decision Process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagen...
Patrice Perny, Paul Weng