Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Mazo Jr., Manuel"'
Autonomous systems typically leverage layered control architectures with a combination of discrete and continuous models operating at different timescales. As a result, layered systems form a new class of hybrid systems composed of systems operating
Externí odkaz:
http://arxiv.org/abs/2409.14902
We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to min
Externí odkaz:
http://arxiv.org/abs/2409.09812
The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework. We investigate a novel scheme to obtain data-driven abstractions of discrete-time stochastic processes in ter
Externí odkaz:
http://arxiv.org/abs/2404.08344
At the intersection of dynamical systems, control theory, and formal methods lies the construction of symbolic abstractions: these typically represent simpler, finite-state models whose behavior mimics that of an underlying concrete system but are ea
Externí odkaz:
http://arxiv.org/abs/2402.10668
We introduce a framework for the control of discrete-time switched stochastic systems with uncertain distributions. In particular, we consider stochastic dynamics with additive noise whose distribution lies in an ambiguity set of distributions that a
Externí odkaz:
http://arxiv.org/abs/2212.14260
A common technique to verify complex logic specifications for dynamical systems is the construction of symbolic abstractions: simpler, finite-state models whose behaviour mimics the one of the systems of interest. Typically, abstractions are construc
Externí odkaz:
http://arxiv.org/abs/2211.01793
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for con
Externí odkaz:
http://arxiv.org/abs/2211.01231
Policy robustness in Reinforcement Learning may not be desirable at any cost: the alterations caused by robustness requirements from otherwise optimal policies should be explainable, quantifiable and formally verifiable. In this work we study how pol
Externí odkaz:
http://arxiv.org/abs/2209.15320
Autor:
Ornia, Daniel Jarne, Mazo Jr, Manuel
Publikováno v:
Formal Modeling and Analysis of Timed Systems. FORMATS 2022. Lecture Notes in Computer Science, vol 13465. Springer, Cham
We present an approach to reduce the communication required between agents in a Multi-Agent learning system by exploiting the inherent robustness of the underlying Markov Decision Process. We compute so-called robustness surrogate functions (off-line
Externí odkaz:
http://arxiv.org/abs/2204.03361
Autor:
Peruffo, Andrea, Mazo Jr, Manuel
We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario approach to multiclass SVM
Externí odkaz:
http://arxiv.org/abs/2203.05522