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136
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CORR
2010
Springer
106views Education» more  CORR 2010»
15 years 5 months ago
MDPs with Unawareness
Markov decision processes (MDPs) are widely used for modeling decision-making problems in robotics, automated control, and economics. Traditional MDPs assume that the decision mak...
Joseph Y. Halpern, Nan Rong, Ashutosh Saxena
142
Voted
FUIN
2002
63views more  FUIN 2002»
15 years 5 months ago
Probabilistic Cluster Unfoldings
Abstract. This article introduces probabilistic cluster branching processes, a probabilistic unfolding semantics for untimed Petri nets, with no structural or safety assumptions, g...
Stefan Haar
JMLR
2002
78views more  JMLR 2002»
15 years 5 months ago
Shallow Parsing using Specialized HMMs
We present a unified technique to solve different shallow parsing tasks as a tagging problem using a Hidden Markov Model-based approach (HMM). This technique consists of the incor...
Antonio Molina, Ferran Pla
143
Voted
ML
2002
ACM
121views Machine Learning» more  ML 2002»
15 years 5 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh
ICML
2010
IEEE
15 years 3 months ago
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
Frank Dondelinger, Sophie Lebre, Dirk Husmeier