Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Partially observable Markov decision processes (POMDPs) have been
successfully applied to various robot motion planning tasks under uncertainty.
However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
Existing algorithms for discrete partially observable Markov decision processes can at best solve problems of a few thousand states due to two important sources of intractability:...
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...