no code implementations • 4 Jan 2021 • Heiko Becker, Nathaniel Bos, Ivan Gavran, Eva Darulova, Rupak Majumdar
We present Lassie, a tactic framework for the HOL4 theorem prover that allows individual users to define their own tactic language by example and give frequently used tactics or tactic combinations easier-to-remember names.
Automated Theorem Proving Programming Languages
no code implementations • 12 Sep 2019 • Zhe Xu, Ivan Gavran, Yousef Ahmad, Rupak Majumdar, Daniel Neider, Ufuk Topcu, Bo Wu
The experiments show that learning high-level knowledge in the form of reward machines can lead to fast convergence to optimal policies in RL, while standard RL methods such as q-learning and hierarchical RL methods fail to converge to optimal policies after a substantial number of training steps in many tasks.
no code implementations • 6 Mar 2018 • Ivan Gavran, Brendon Boldt, Eva Darulova, Rupak Majumdar
We present Flipper, a natural language interface for describing high-level task specifications for robots that are compiled into robot actions.