Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
Reinforcement learning for intelligent healthcare systems: A review of challenges, applications, and open research issues
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …
expenditure and mortality rates. This calls for transforming healthcare systems away from …
Smooth operator: Control using the smooth robustness of temporal logic
Modern control systems, like controllers for swarms of quadrotors, must satisfy complex
control objectives while withstanding a wide range of disturbances, from bugs in their …
control objectives while withstanding a wide range of disturbances, from bugs in their …
Arithmetic-geometric mean robustness for control from signal temporal logic specifications
We present a new average-based robustness for Signal Temporal Logic (STL) and a
framework for optimal control of a dynamical system under STL constraints. By averaging the …
framework for optimal control of a dynamical system under STL constraints. By averaging the …
Temporal logics for learning and detection of anomalous behavior
The increased complexity of modern systems necessitates automated anomaly detection
methods to detect possible anomalous behavior determined by malfunctions or external …
methods to detect possible anomalous behavior determined by malfunctions or external …
Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles
CE Tuncali, TP Pavlic… - 2016 ieee 19th …, 2016 - ieeexplore.ieee.org
This paper proposes an approach to automatically generating test cases for testing motion
controllers of autonomous vehicular systems. Test scenarios may consist of single or …
controllers of autonomous vehicular systems. Test scenarios may consist of single or …
Effective hybrid system falsification using Monte Carlo tree search guided by QB-robustness
Hybrid system falsification is an important quality assurance method for cyber-physical
systems with the advantage of scalability and feasibility in practice than exhaustive …
systems with the advantage of scalability and feasibility in practice than exhaustive …
Testing cyber-physical systems through bayesian optimization
Many problems in the design and analysis of cyber-physical systems (CPS) reduce to the
following optimization problem: given a CPS which transforms continuous-time input traces …
following optimization problem: given a CPS which transforms continuous-time input traces …
Reinforcement learning for intelligent healthcare systems: A comprehensive survey
The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
Falsification of cyber-physical systems using deep reinforcement learning
T Akazaki, S Liu, Y Yamagata, Y Duan… - … , FM 2018, Held as Part of …, 2018 - Springer
With the rapid development of software and distributed computing, Cyber-Physical Systems
(CPS) are widely adopted in many application areas, eg, smart grid, autonomous …
(CPS) are widely adopted in many application areas, eg, smart grid, autonomous …