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AAAI
1997
13 years 9 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
CDC
2009
IEEE
131views Control Systems» more  CDC 2009»
13 years 11 months ago
Further results on plant parameter identification using continuous-time multiple-model adaptive estimators
This paper describes a deterministic approach to adaptive state and parameter estimation using a multiple model structure. In the set-up adopted, the plant of interest is described...
Vahid Hassani, A. Pedro Aguiar, António Man...
HYBRID
2010
Springer
14 years 2 months ago
On integration of event-based estimation and robust MPC in a feedback loop
The main purpose of event-based control, if compared to periodic control, is to minimize data transfer or processing power in networked control systems. Current methods have an (i...
Joris Sijs, Mircea Lazar, W. P. M. H. Heemels
EWRL
2008
13 years 9 months ago
Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
Kirill Dyagilev, Shie Mannor, Nahum Shimkin
ACSC
2005
IEEE
14 years 1 months ago
A Pedagogical Evaluation of New State Model Diagrams for Teaching Internetwork Technologies
Curriculum based on internetworking devices is primarily based on the Command Line Interface (CLI) and case studies. However a single CLI command may produce output that is not on...
Stanislaw P. Maj, G. Kohli, T. Fetherston