@conference {
	title = {Cooperative Hypothesis Testing by Two Observers with Asymmetric Information},
	author = {Raghavan, Aneesh., Baras, John S.},
	year = {2019},
	month = {07/2019},
	address = {Akko, Israel},
	booktitle = {27th Mediterranean Conference on Control \& Automation}
}
@conference {
	title = {Non-Commutative Probability Models in Human Decision Making: Binary Hypothesis Testing},
	author = {Raghavan, Aneesh., Baras, John S.},
	year = {2018},
	month = {12/2018},
	abstract = {In this paper, we consider the binary hypothesis testing problem, as the simplest human
decision making problem, using a von-Neumann non-commutative probability framework.
We present two approaches to this decision making problem. In the rst approach, we represent
the available data as coming from measurements modeled via projection valued measures (PVM)
and retrieve the results of the underlying detection problem solved using classical probability
models. In the second approach, we represent the measurements using positive operator valued
measures (POVM). We prove that the minimum probability of error achieved in the second
approach is the same as in the rst approach.},
	address = {Miami, Florida},
	booktitle = {2nd IFAc Conference on Cyber-Physical \& Human-Systems (CPHS)2018}
}
@conference {
	title = {Stochastic Control Formulation of The Car Overtake Problem},
	author = {Raghavan, Aneesh., Wei, Jieqiang., Baras, John S., Johansson, Karl Henrik.},
	year = {2018},
	month = {06/2018},
	abstract = {~In this paper, we consider the classic car overtake problem. There are three cars, two moving along the same direction in the same lane while the third car moves in the direction opposite to that of the first two cars in the adjacent lane. The objective of the trailing car is to overtake the car in front of it avoiding collision with the other cars in the scenario. The information available to the trailing car is the relative position, relative velocities with respect to other cars and its position and past actions. The relative position and relative velocity information is corrupted by noise. Given this information, the car needs to make a decision as to whether it wants to overtake or not. We present a control algorithm for the car which minimizes the probability of collision with both the cars. We also present the results obtained by simulating the above scenario with the control algorithm. Through simulations we study the effect of the variance of the measurement noise and the time at which the decision is made on the probability of collision.},
	address = {Savona, Italy},
	booktitle = {15th IFAC Symposium on Control in Transportation Systems},
	pages = {pp. 124-129}
}
@conference {
	title = {Collaborative Sequential Detection of Gaussian Models from Observed Data and The Value of Information Exchanged},
	author = {Raghavan, Aneesh., Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {We consider the problem of detecting which Gaussian model generates an observed time series data. We consider as possible generative models two linear systems driven by white Gaussian noise with Gaussian initial conditions. We also consider two collaborating observers. The observers observe a function of the state of the systems. Using these observations, the aim is to find which one of the two Gaussian models has generated the observations. For each observer we formulate a sequential hypothesis testing problem. Each observer computes its own likelihood ratio based on its own observations.Using the likelihood ratio, each observer performs sequential probability ratio test (SPRT) to arrive at its decision on the hypothesis. Taking into account the random and asymmetric stopping times of the two observers, we present a consensus algorithm which guarantees asymptotic convergence to the true hypothesis. The consensus algorithm involves exchange of information, i.e., the decision of the observers. Through simulations, the {\textquotedblleft}value{\textquotedblright} of the information exchanged, probability of error and average time to consensus are computed.~},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 15-20}
}
@conference {
	title = {Detection of Markov Chain Models from Observed Data Using Binary Hypothesis Testing and Consensus:},
	author = {Raghavan, Aneesh., Baras, John S.},
	year = {2017},
	month = {07/2017},
	abstract = {In this paper, we consider the problem of detecting which Markov chain model generates observed time series data. We consider two Markov chains. The state of the Markov chain cannot be observed directly, only a function of the state can be observed. Using these observations, the aim is to find which of the two Markov chains has generated the observations. We consider two observers. Each observer observes a function of the state of the Markov chains. We formulate a binary hypothesis testing problem for each observer. Each observer makes a decision on the hypothesis based on its observations. Then Observer 1 communicates its decision to Observer 2 and vice-versa. If the decisions are the same, then a consensus has been achieved. If their decisions are different then the binary hypothesis testing problem is continued. This process is repeated until consensus has been achieved. We solve the binary hypothesis testing problem and prove the convergence of the consensus algorithm. The {\textquotedblleft}value{\textquotedblright} of the information gained through 1-bit communication is discussed along with simulation results.},
	address = {Toulouse, France},
	booktitle = {IFAC 2017 World Conference},
	pages = {pp. 3944-3949}
}
@conference {
	title = {Stochastic Differential Linear-Quadratic Games with Intermittent Asymmetric Observations},
	author = {Maity, Dipankar., Raghavan, Aneesh., Baras, John S.},
	year = {2017},
	month = {05/2017},
	abstract = {In this paper, we consider a two-players stochastic linear quadratic game framework. The game is partially observed and each player has their own private observation. The challenge is that none of the players has access to the continuum observations, rather they can access their respective observations at discrete time instances by operating a switch unanimously. The operation of the switch is costly and hence the gathering of the observations are costly. Each player is equipped with finite memory and she can only use the latest observation to construct the control strategy. The private observations of the players lead to a source of asymmetry in this game. Moreover, the players have different costs for operating the switch, which is another source of asymmetry. We study the structural properties of the Nash equilibrium for this particular class of problems and then we finally show that the switching problem simplifies to a bi-objective optimization problem.},
	address = {Seattle, WA},
	booktitle = {2017 American Control Conference},
	pages = {pp. 3670-3675},
	organization = {Sheraton Seattle Hotel}
}
@conference {
	title = {Time varying Control set design for UAV Collision Avoidance Using Reachable Tubes},
	author = {Zhou, Yuchen., Raghavan, Aneesh., Baras, John S.},
	year = {2016},
	month = {12/2016},
	abstract = {In this paper, we propose a reachable set based~collision avoidance algorithm for unmanned aerial vehicles (UAVs). UAVs have been deployed for agriculture research and~management, surveillance and sensor coverage for threat detection~and disaster search and rescue operations. It is essential for~the aircraft to have on-board collision avoidance capability to~guarantee safety. Instead of the traditional approach of collision~avoidance between trajectories, we propose a collision avoidance~scheme based on reachable sets and tubes. We then formulate~the problem as a convex optimization problem seeking time~varying control sets for ego aircraft giving predicted intruder~reachable tube. We have applied the approach on a case study~of two quadrotors collision avoidance scenario.},
	address = {Las Vegas, Nevada},
	booktitle = {55th IEEE Conference on Decision and Control },
	pages = {pp. 6857-6862}
}
@conference {
	title = {Noncommutative Probability Models for Net-CPS and Net-CPSS},
	author = {Baras, John S., Raghavan, Aneesh.},
	year = {2016},
	month = {09/2016},
	address = {Garching, Munich},
	booktitle = {International Symposium on Networked Cyber-Physical Systems (NetCPS)}
}
