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ICML
1994
IEEE
13 years 11 months ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
CVIU
2010
163views more  CVIU 2010»
13 years 7 months ago
Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process
This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal ...
Jesse Hoey, Pascal Poupart, Axel von Bertoldi, Tam...
DKE
2008
88views more  DKE 2008»
13 years 7 months ago
Quantifying process equivalence based on observed behavior
In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matc...
Ana Karla Alves de Medeiros, Wil M. P. van der Aal...
AAAI
1996
13 years 8 months ago
Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Craig Boutilier, David Poole
MFCS
2010
Springer
13 years 6 months ago
Qualitative Analysis of Partially-Observable Markov Decision Processes
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observationbased strategy relies on partial information...
Krishnendu Chatterjee, Laurent Doyen, Thomas A. He...