Leandro Chaves Rêgo

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There has been a great deal of work on characterizing the complexity of the satisfiability and validity problem for modal logics. In particular, Ladner showed that the satisfiability problem for all logics betweenK andS4 is PSPACE-hard, while forS5 it is NP-complete. We show that it is negative introspection, the axiom ¬Kp ⇒ K¬Kp, that causes the gap: if we(More)
We analyze a model of interactive unawareness introduced by Heifetz, Meier and Schipper (HMS). We consider two axiomatizations for their model, which capture different notions of validity. These axiomatizations allow us to compare the HMS approach to both the standard (S5) epistemic logic and two other approaches to unawareness: that of Fagin and Halpern(More)
A Chaotic Probability model is a usual set of probability measures, M, the totality of which is endowed with an objective, frequentist interpretation as opposed to being viewed as a statistical compound hypothesis or an imprecise behavioral subjective one. In the prior work of Fierens and Fine, given finite time series data, the estimation of the Chaotic(More)
In earlier work [Halpern and Rêgo 2006b], we proposed a logic that extends the Logic of General Awareness of Fagin and Halpern [1988] by allowing quantification over primitive propositions. This makes it possible to express the fact that an agent knows that there are some facts of which he is unaware. In that logic, it is not possible to model an agent(More)
Most work in game theory assumes that players are perfect reasoners and have common knowledge of all significant aspects of the game. In earlier work [Halpern and Rêgo 2006], we proposed a framework for representing and analyzing games with possibly unaware players, and suggested a generalization of Nash equilibrium appropriate for games with unaware(More)
Applying unawareness belief structures introduced in Heifetz, Meier, and Schipper (2013a), we develop Bayesian games with unawareness, define equilibrium, and prove existence. We show how equilibria are extended naturally from lower to higher awareness levels and restricted from higher to lower awareness levels. We apply Bayesian games with unawareness to(More)
We study the performance of a decision feedback decoder (DFD) for convolutional codes over an interleaved burst channel. The DFD adaptively estimates channel reliability information from previous decisions. The effect of ermr propagation, finite interleaving and the length of the initial training sequence is explicitly studied. A binary Gilbert-Elliott(More)