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Found 10 papers, 2 papers with code
Adaptive ECCM for Mitigating Smart Jammers
This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer.
How can a Radar Mask its Cognition?
We provide theoretical guarantees by ensuring the Type-I error probability of the adversary's detector exceeds a pre-defined level for a specified tolerance on the radar's performance loss.
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner
In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL).
Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars
A meta-cognitive radar is aware of the adversarial nature of the target and seeks to mitigate the adversarial target.
How can a Cognitive Radar Mask its Cognition?
In turn, the radar deliberately chooses sub-optimal responses so that its utility function almost fails the utility maximization test, and hence, its cognitive ability is masked from the adversary.
Unifying Revealed Preference and Revealed Rational Inattention
Second, we exploit the unification result computationally to extend robustness measures for goodness-of-fit of revealed preference tests in the literature to revealed rational inattention.
Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification
Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs?
Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference
The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level and revealed preferences for identifying utility maximization at the systems level.
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems.
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers.
