Secure Control in Partially Observable Environments to Satisfy LTL Specifications
July 22, 2020 ยท Declared Dead ยท ๐ IEEE Transactions on Automatic Control
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bhaskar Ramasubramanian, Luyao Niu, Andrew Clark, Linda Bushnell, Radha Poovendran
arXiv ID
2007.12501
Category
eess.SY: Systems & Control (EE)
Cross-listed
cs.CR,
cs.GT,
cs.LO
Citations
10
Venue
IEEE Transactions on Automatic Control
Last Checked
1 month ago
Abstract
This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. The search for policies is limited to the space of finite state controllers, which leads to a tractable approach to determine policies. We relate the satisfaction of the specification to reaching (a subset of) recurrent states of a Markov chain. We present an algorithm to determine a set of defender and adversary finite state controllers of fixed sizes that will satisfy the temporal logic specification, and prove that it is sound. We then propose a value-iteration algorithm to maximize the probability of satisfying the temporal logic specification under finite state controllers of fixed sizes. Lastly, we extend this setting to the scenario where the size of the finite state controller of the defender can be increased to improve the satisfaction probability. We illustrate our approach with an example.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Systems & Control (EE)
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey
R.I.P.
๐ป
Ghosted
Wireless Network Design for Control Systems: A Survey
R.I.P.
๐ป
Ghosted
Learning-based Model Predictive Control for Safe Exploration
R.I.P.
๐ป
Ghosted
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
R.I.P.
๐ป
Ghosted
Novel Multidimensional Models of Opinion Dynamics in Social Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted