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20132022
most citedComparison of Algorithms for Simple Stochastic Games

8 citations · 22 across the 7 of their papers we have counts for

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14 papers · 1 filter

cs.LO2022

Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes

Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský +1

We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and compu…

cs.LO2020

DeepAbstract: Neural Network Abstraction for Accelerating Verification

Pranav Ashok, Vahid Hashemi, Jan Křetínský +1

While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks. However, it can help with the still open task of scalin…

cs.LO2019

Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis

Jan Křetínský, Alexander Manta, Tobias Meggendorfer

We propose "semantic labelling" as a novel ingredient for solving games in the context of LTL synthesis. It exploits recent advances in the automata-based approach, yielding more i…

cs.LO2019

Strategy Representation by Decision Trees with Linear Classifiers

Pranav Ashok, Tomáš Brázdil, Krishnendu Chatterjee +3

Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of -regular w…

cs.LO2018

Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes

Pranav Ashok, Tomáš Brázdil, Jan Křetínský +1

The maximum reachability probabilities in a Markov decision process can be computed using value iteration (VI). Recently, simulation-based heuristic extensions of VI have been intr…

cs.LO2018

LTL Store: Repository of LTL formulae from literature and case studies

Jan Křetínský, Tobias Meggendorfer, Salomon Sickert

This continuously extended technical report collects and compares commonly used formulae from the literature and provides them in a machine readable way.