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ICML
1999
IEEE
14 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
AAAI
1994
13 years 9 months ago
Acting Optimally in Partially Observable Stochastic Domains
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...
ATAL
2004
Springer
14 years 1 months ago
Interactive POMDPs: Properties and Preliminary Results
This paper presents properties and results of a new framework for sequential decision-making in multiagent settings called interactive partially observable Markov decision process...
Piotr J. Gmytrasiewicz, Prashant Doshi
ICTAI
1996
IEEE
13 years 11 months ago
Incremental Markov-Model Planning
This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. T...
Richard Washington
PERCOM
2007
ACM
14 years 7 months ago
Sensor Scheduling for Optimal Observability Using Estimation Entropy
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
Mohammad Rezaeian