@conference {
	title = {Linear Quadratic Games with Costly Measurements},
	author = {Maity, Dipankar., Anastasopoulos, Achilleas. , Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {In this work we consider a stochastic linear quadratic two-player game.The state measurements are observed through a switched noiseless communication link. Each player incurs a finite cost every time the link is established to get measurements. Along with the usual control action, each player is equipped with a switching action to control the communication link. The measurements help to improve the estimate and hence reduce the quadratic cost, but at the same time the cost is increased due to switching. We study the subgame perfect equilibrium control and switching strategies for the players. We show that the problem can be solved in a two-step process by solving two dynamic programming problems. The first step corresponds to solving a dynamic programming for the control strategy and the second step involves solving another dynamic programming for the switching strategy.},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 6223-6228}
}
@conference {
	title = {Algebraic-Graphical Approach for Signed Dynamical Networks},
	author = {Shi, Guodong., Altafini, Claudio., Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {A signed network is a network with each link associated with a positive or negative sign. Models for nodes interacting over such signed networks, where two types of interactions are defined along the positive and negative links, respectively, arise from various biological, social, political, and economical systems. Starting from standard consensus dynamics, there are two basic types of negative interactions along negative links, namely state flipping or relative-state flipping. In this paper, we provide an algebraic-graphical method serving as a systematic tool of studying these dynamics over signed networks. Utilizing generalized Perron-Frobenius theory, graph theory, and elementary algebraic recursions, we show this method is useful to establish a series of basic convergence results for dynamics over signed networks.
~},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 2009-2014}
}
@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 = {Networked H$\infty$ Control with Minimum Directed Information},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2017},
	month = {12/2017},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)}
}
@conference {
	title = {Crowdsourcing with Multi-Dimensional Trust and Active Learning},
	author = {Liu, Xiangyang., Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {We consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from users, trust is used to model workers expertise. We propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions,and true labels of questions.Our model is flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. In order to decrease entropies and reduce error rates more quickly with fewer annotations from workers, we further propose strategies for selecting which questions to ask and which workers to assign the questions to based on multidimension characteristics of questions and workers trust values in those dimensions. We evaluate our models and algorithms on real-world data sets. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them.~},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 3224-3231}
}
@conference {
	title = {Event-Triggered Output-Feedback H$\infty$ Control with Minimum Directed Information},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {In this paper, the H$\infty$ control problem with minimum information exchange between a plant and a remote controller is considered. For transmission of the measured outputs, an event-triggering mechanism is proposed. We measure the transferred information satisfying the causality condition by the directed information, and measure the H$\infty$ control performance by the L2-gain. We achieve the minimum directed information required for a guaranteed level of control performance. In particular, corresponding to the worst case disturbances, we develop a filter with a recursive variable, and show that the optimal control policy is a linear function of this recursive variable. Finally, we define the value of information, and determine the structure of the optimal sampling policy based on the value of information.
~},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 6088-6094}
}
