Probabilistic model checking and autonomy
M Kwiatkowska, G Norman… - Annual review of control …, 2022 - annualreviews.org
The design and control of autonomous systems that operate in uncertain or adversarial
environments can be facilitated by formal modeling and analysis. Probabilistic model …
environments can be facilitated by formal modeling and analysis. Probabilistic model …
Cost-effective moving target defense against DDoS attacks using trilateral game and multi-objective Markov decision processes
Abstract Moving Target Defense (MTD) has emerged as a game changer to reverse the
asymmetric situation between attackers and defenders, and as one of the most effective …
asymmetric situation between attackers and defenders, and as one of the most effective …
Robot search path planning method based on prioritized deep reinforcement learning
Y Liu, Z Chen, Y Li, M Lu, C Chen, X Zhang - International Journal of …, 2022 - Springer
The path planning process of the robot relies too much on environmental information, which
makes it difficult to obtain the optimal search path when the search and rescue tasks are …
makes it difficult to obtain the optimal search path when the search and rescue tasks are …
Parameter Synthesis for Markov Models: Covering the Parameter Space
Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …
probabilities in Markov models need to be known. However, system quantities such as …
Formal verification of unknown dynamical systems via Gaussian process regression
Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …
behaviors in the presence of uncertainties and black-box components that influence the …
Gaussian belief trees for chance constrained asymptotically optimal motion planning
QH Ho, ZN Sunberg… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
In this paper, we address the problem of sampling-based motion planning under motion and
measurement un-certainty with probabilistic guarantees. We generalize traditional sampling …
measurement un-certainty with probabilistic guarantees. We generalize traditional sampling …
Strategy synthesis for partially-known switched stochastic systems
We present a data-driven framework for strategy synthesis for partially-known switched
stochastic systems. The properties of the system are specified using linear temporal logic …
stochastic systems. The properties of the system are specified using linear temporal logic …
Abstraction-based synthesis for stochastic systems with omega-regular objectives
This paper studies the synthesis of controllers for discrete-time, continuous state stochastic
systems subject to omega-regular specifications using finite-state abstractions. Omega …
systems subject to omega-regular specifications using finite-state abstractions. Omega …
Parameter synthesis for Markov models: covering the parameter space
Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …
probabilities in Markov models need to be known. However, system quantities such as …
Multi-objective optimization of long-run average and total rewards
T Quatmann, JP Katoen - Tools and Algorithms for the Construction and …, 2021 - Springer
This paper presents an efficient procedure for multi-objective model checking of long-run
average reward (aka: mean pay-off) and total reward objectives as well as their combination …
average reward (aka: mean pay-off) and total reward objectives as well as their combination …