51 citations · 122 across the 7 of their papers we have counts for
10 papers
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts
Pranav Ashok, Mathias Jackermeier, Jan Křetínský +3
Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make…
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…
dtControl: Decision Tree Learning Algorithms for Controller Representation
Pranav Ashok, Mathias Jackermeier, Pushpak Jagtap +3
Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent…
Approximating Values of Generalized-Reachability Stochastic Games
Pranav Ashok, Krishnendu Chatterjee, Jan Kretinsky +2
Simple stochastic games are turn-based 2.5-player games with a reachability objective. The basic question asks whether one player can ensure reaching a given target with at least a…
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…
SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes
Pranav Ashok, Jan Křetínský, Kim Guldstrand Larsen +3
For hybrid Markov decision processes, UPPAAL Stratego can compute strategies that are safe for a given safety property and (in the limit) optimal for a given cost function. Unfortu…