@conference {
	title = {Gibbs Sampler Based Control of Autonomous Vehicle Swarms in the Presence of Sensor Errors},
	author = {Xi, Wei., Baras, John S.},
	year = {2006},
	month = {12/2006},
	abstract = {In earlier work of the authors[1], [2], [3], [4], [5], it was shown that Gibbs sampler based annealing algorithms could be used for vehicle swarms to achieve self-organization. Nevertheless, the earlier convergence analyses were based on the assumption that the Gibbs potential can be precisely evaluated. In practice, Gibbs potentials have to be measured via sensors, which usually introduce errors. The robustness of the stochastic algorithm under sensor errors is studied in this paper. Two types of sensor error, range-error and random error, are investigated. Analytical results on convergence are derived for sensor errors with limited support, and are further validated through simulations.},
	address = {San Diego, CA},
	booktitle = {45th IEEE Conference on Decision and Control},
	pages = {pp. 5084-5090}
}
@conference {
	title = {Multiple Sampling for Estimation on a Finite Horizon},
	author = {Rabi, Maben., Moustakides, George V., Baras, John S.},
	year = {2006},
	month = {12/2006},
	abstract = {We discuss some multiple sampling problems that arise in finite horizon real-time estimation when there is an upper limit on the number of allowable samples. Measuring estimation quality by the aggregate squared error, we compare the performances of the best deterministic, level-triggered and the optimal sampling schemes. We restrict the signal to be either a Wiener or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions, whereas for the Ornstein Uhlenbeck process, procedures for numerical computation. Our results indicate that the best level-triggered sampling is almost optimal when the signal is stable.},
	address = {San Diego, CA},
	booktitle = {45th IEEE Conference on Decision and Control},
	pages = {pp. 1351-1357}
}
@conference {
	title = {Maximum Entropy Models, Dynamic Games and Robust Output Feedback Control of Nonlinear Systems},
	author = {Baras, John S.},
	year = {2006},
	month = {12/2006},
	abstract = {In this paper, we develop a framework for designing controllers for general, partially observed discretetime nonlinear systems which are robust with respect to uncertainties (disturbances). A general deterministic model for uncertainties is introduced, leading to a dynamic game formulation of the robust control problem. This problem is solved using an appropriate information state. We derive a partially observed nonlinear stochastic model as the maximum entropy stochastic model for the nonlinear system. A risk-sensitive stochastic control problem is formulated and solved for this partially observed stochastic model. The two problems are related using small noise limits. These small noise asymptotics are for the first time justified as the appropriate randomization, using time asymptotics of the Lagrange multipliers involved in the maximum entropy model construction. Thus for the first time a complete unification of deterministic and randomized uncertainty models is achieved. Various interpretations, consequences and applications of this unification are explained and discussed.},
	address = {San Diego, CA},
	booktitle = {45th IEEE Conference on Decision and Control},
	pages = {pp. 4899-4906}
}
