@conference {6580549,
	title = {Sampling-based algorithms for continuous-time POMDPs},
	booktitle = {American Control Conference (ACC), 2013},
	year = {2013},
	month = {June},
	abstract = {<p>This paper focuses on a continuous-time, continuous-space formulation of the stochastic optimal control problem with nonlinear dynamics and observation noise. We lay the mathematical foundations to construct, via incremental sampling, an approximating sequence of discrete-time finite-state partially observable Markov decision processes (POMDPs), such that the behavior of successive approximations converges to the behavior of the original continuous system in an appropriate sense. We also show that the optimal cost function and control policies for these POMDP approximations converge almost surely to their counterparts for the underlying continuous system in the limit. We demonstrate this approach on two popular continuous-time problems, viz., the Linear-Quadratic-Gaussian (LQG) control problem and the light-dark domain problem.</p>},
	keywords = {Approximation methods, approximation theory, continuous time systems, continuous-space formulation, continuous-time formulation, continuous-time POMDP, Cost function, cost optimal control, discrete time systems, discrete-time finite-state partially observable Markov decision processes, incremental sampling-based algorithms, light-dark domain problem, linear quadratic Gaussian control, linear-quadratic-Gaussian control problem, LQG, Manganese, Markov processes, mathematical foundations, nonlinear control systems, nonlinear dynamics, observation noise, optimal cost control policies, optimal cost function policies, sampling methods, sequence approximation, stochastic optimal control problem, stochastic systems, Tin, Trajectory},
	doi = {10.1109/ACC.2013.6580549},
	author = {Chaudhari, P. and Sertac Karaman and Hsu, D. and Emilio Frazzoli}
}