98 search results - page 5 / 20 » Using Rewards for Belief State Updates in Partially Observab... |
CSL
2012
Springer
12 years 3 months ago
2012
Springer
Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estim...
IJCAI
2007
13 years 9 months ago
2007
We consider the fundamental problem of monitoring (i.e. tracking) the belief state in a dynamic system, when the model is only approximately correct and when the initial belief st...
IJCAI
2003
13 years 9 months ago
2003
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
IJCAI
2001
13 years 9 months ago
2001
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
PERCOM
2007
ACM
14 years 7 months ago
2007
ACM
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 ...