Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Kretinsky, Jan"'
Ranking risks and countermeasures is one of the foremost goals of quantitative security analysis. One of the popular frameworks, used also in industrial practice, for this task are attack-defense trees. Standard quantitative analyses available for at
Externí odkaz:
http://arxiv.org/abs/2406.15605
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable gap exists due to the discreteness of symbolic representations and th
Externí odkaz:
http://arxiv.org/abs/2405.14389
Autor:
Andriushchenko, Roman, Bork, Alexander, Budde, Carlos E., Češka, Milan, Grover, Kush, Hahn, Ernst Moritz, Hartmanns, Arnd, Israelsen, Bryant, Jansen, Nils, Jeppson, Joshua, Junges, Sebastian, Köhl, Maximilian A., Könighofer, Bettina, Křetínský, Jan, Meggendorfer, Tobias, Parker, David, Pranger, Stefan, Quatmann, Tim, Ruijters, Enno, Taylor, Landon, Volk, Matthias, Weininger, Maximilian, Zhang, Zhen
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected reward
Externí odkaz:
http://arxiv.org/abs/2405.13583
Autor:
Azeem, Muqsit, Grobelna, Marta, Kanav, Sudeep, Kretinsky, Jan, Mohr, Stefanie, Rieder, Sabine
The behavior of neural networks (NNs) on previously unseen types of data (out-of-distribution or OOD) is typically unpredictable. This can be dangerous if the network's output is used for decision-making in a safety-critical system. Hence, detecting
Externí odkaz:
http://arxiv.org/abs/2405.10350
Autor:
Brázdil, Tomáš, Chatterjee, Krishnendu, Chmelik, Martin, Forejt, Vojtěch, Křetínský, Jan, Kwiatkowska, Marta, Meggendorfer, Tobias, Parker, David, Ujma, Mateusz
We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of th
Externí odkaz:
http://arxiv.org/abs/2403.09184
Strategies for partially observable Markov decision processes (POMDP) typically require memory. One way to represent this memory is via automata. We present a method to learn an automaton representation of a strategy using a modification of the L*-al
Externí odkaz:
http://arxiv.org/abs/2401.07656
Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar
Externí odkaz:
http://arxiv.org/abs/2307.10891
We present MULTIGAIN 2.0, a major extension to the controller synthesis tool MULTIGAIN, built on top of the probabilistic model checker PRISM. This new version extends MULTIGAIN's multi-objective capabilities, by allowing for the formal verification
Externí odkaz:
http://arxiv.org/abs/2305.16752
We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-eff
Externí odkaz:
http://arxiv.org/abs/2305.15109
A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approach is typically preferred over exact techniques, even though no practi
Externí odkaz:
http://arxiv.org/abs/2304.09930