Predictive monitoring—making predictions about future states and monitoring if the
predicted states satisfy requirements—offers a promising paradigm in supporting the …

We present a framework to interpret signal temporal logic (STL) formulas over discrete-time
stochastic processes in terms of the induced risk. Each realization of a stochastic process …

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …

We present a policy gradient method for Multi-Objective Reinforcement Learning under
unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for …

Autonomous Systems Design: Charting a New Discipline Page 1 8 2168-2364/21©2021 IEEE
Copublished by the IEEE CEDA, IEEE CASS, IEEE SSCS, and TTTC IEEE Design&Test …

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs)
under a global signal temporal logic (STL) specification to be satisfied at a predefined …

Temporal logic inference is the process of extracting formal descriptions of system behaviors
from data in the form of temporal logic formulas. The existing temporal logic inference …

Shape expressions (SEs) is a novel specification language that was recently introduced to
express behavioral patterns over real-valued signals observed during the execution of cyber …

We propose a new technique for performing state-space exploration of closed-loop control
systems with neural network feedback controllers. Our approach involves approximating the …

Cyberphysical systems (CPSs) are vulnerable to catastrophic fault propagation due to the
strong connectivity among their subsystems. This article introduces a learning-based …