Conference paper
Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
The most natural, compositional, way of modeling real-time systems uses a dense domain for time. The satisfiability of timing constraints that are capable of expressing punctuality in this model, however, is known to be undecidable. We introduce a temporal language that can constrain the time difference between events only with finite, yet arbitrary, precision and show the resulting logic to be EXPSPACE-complete. This result allows us to develop an algorithm for the verification of timing properties of real-time systems with a dense semantics.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Shai Fine, Yishay Mansour
Machine Learning