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Found 8 papers, 0 papers with code
Entropy-Regularized Partially Observed Markov Decision Processes
We investigate partially observed Markov decision processes (POMDPs) with cost functions regularized by entropy terms describing state, observation, and control uncertainty.
Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs
We study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory.
Granger Causality from Quantized Measurements
Then, conditions are derived under which Granger causality between jointly Gaussian processes can be reliably inferred from the second order moments of quantized measurements.
Active Trajectory Estimation for Partially Observed Markov Decision Processes via Conditional Entropy
By establishing a novel form of the smoother entropy in terms of the POMDP belief (or information) state, we show that our active smoothing problem can be reformulated as a (fully observed) Markov decision process with a value function that is concave in the belief state.
Smoothing-Averse Control: Covertness and Privacy from Smoothers
In this paper we investigate the problem of controlling a partially observed stochastic dynamical system such that its state is difficult to infer using a (fixed-interval) Bayesian smoother.
Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion
A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions.
Bounded State Estimation over Finite-State Channels: Relating Topological Entropy and Zero-Error Capacity
We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver.
Uniformly Bounded State Estimation over Multiple Access Channels
This paper addresses the problem of distributed state estimation via multiple access channels (MACs).
