2 citations · 3 across the 2 of their papers we have counts for
3 papers
Active Learning of Abstract System Models from Traces using Model Checking [Extended]
Natasha Yogananda Jeppu, Tom Melham, Daniel Kroening
We present a new active model-learning approach to generating abstractions of a system implementation, as finite state automata (FSAs), from execution traces. Given an implementati…
Learning Concise Models from Long Execution Traces
Natasha Yogananda Jeppu, Tom Melham, Daniel Kroening +1
Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting usef…
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate +2
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress…