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» Modelling Uncertainty in Agent Programming
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ECML
2005
Springer
15 years 6 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
124
Voted
MICAI
2005
Springer
15 years 6 months ago
Agent-Based Simulation Replication: A Model Driven Architecture Approach
In Multi-agent based simulation (MABS) systems, computational models are built as multi-agent systems (MAS). Replication of these models can contribute to improve the reliability o...
Candelaria Sansores, Juan Pavón
MP
2006
107views more  MP 2006»
15 years 15 days ago
Convergence theory for nonconvex stochastic programming with an application to mixed logit
Monte Carlo methods have been used extensively in the area of stochastic programming. As with other methods that involve a level of uncertainty, theoretical properties are required...
Fabian Bastin, Cinzia Cirillo, Philippe L. Toint
SIAMJO
2002
122views more  SIAMJO 2002»
15 years 6 days ago
Robust Filtering via Semidefinite Programming with Applications to Target Tracking
In this paper we propose a novel finite-horizon, discrete-time, time-varying filtering method based on the robust semidefinite programming (SDP) technique. The proposed method prov...
Lingjie Li, Zhi-Quan Luo, Timothy N. Davidson, Kon...
118
Voted
CDC
2010
IEEE
112views Control Systems» more  CDC 2010»
14 years 7 months ago
Online Convex Programming and regularization in adaptive control
Online Convex Programming (OCP) is a recently developed model of sequential decision-making in the presence of time-varying uncertainty. In this framework, a decisionmaker selects ...
Maxim Raginsky, Alexander Rakhlin, Serdar Yük...