Pranav Ashok | DeepAI

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∙ 01/15/2021

dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts

Recent advances have shown how decision trees are apt data structures fo...

0 Pranav Ashok, et al. ∙

∙ 06/24/2020

DeepAbstract: Neural Network Abstraction for Accelerating Verification

While abstraction is a classic tool of verification to scale it up, it i...

0 Pranav Ashok, et al. ∙

∙ 02/12/2020

dtControl: Decision Tree Learning Algorithms for Controller Representation

Decision tree learning is a popular classification technique most common...

0 Pranav Ashok, et al. ∙

∙ 08/14/2019

Approximating Values of Generalized-Reachability Stochastic Games

Simple stochastic games are turn-based 2.5-player games with a reachabil...

0 Pranav Ashok, et al. ∙

∙ 06/25/2019

SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes

For hybrid Markov decision processes, UPPAAL Stratego can compute strate...

0 Pranav Ashok, et al. ∙

∙ 06/19/2019

Strategy Representation by Decision Trees with Linear Classifiers

Graph games and Markov decision processes (MDPs) are standard models in ...

0 Pranav Ashok, et al. ∙

∙ 05/10/2019

PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games

Statistical model checking (SMC) is a technique for analysis of probabil...

0 Pranav Ashok, et al. ∙

∙ 09/10/2018

Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes

The maximum reachability probabilities in a Markov decision process can ...

0 Pranav Ashok, et al. ∙

∙ 07/25/2018

Continuous-Time Markov Decisions based on Partial Exploration

We provide a framework for speeding up algorithms for time-bounded reach...

0 Pranav Ashok, et al. ∙

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