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...
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...
This paper presents properties and results of a new framework for sequential decision-making in multiagent settings called interactive partially observable Markov decision process...
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...
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 ...