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Statistical Detection of Coordination in a Cognitive Radar Network through Inverse Multi-objective Optimization
By 'coordination' we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total network power output.
Finite-Sample Bounds for Adaptive Inverse Reinforcement Learning using Passive Langevin Dynamics
This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics algorithm (PSGLD) designed to achieve adaptive inverse reinforcement learning (IRL).
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
This paper provides a novel multi-objective inverse reinforcement learning approach which allows for both detection of such Pareto optimal ('coordinating') behavior and subsequent reconstruction of each radar's utility function, given a finite dataset of radar network emissions.
Quickest Detection for Human-Sensor Systems using Quantum Decision Theory
In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law).
Lyapunov based Stochastic Stability of a Quantum Decision System for Human-Machine Interaction
We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically.
Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach
We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically.
