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Found 3 papers, 0 papers with code
Lassie: HOL4 Tactics by Example
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
Joint Inference of Reward Machines and Policies for Reinforcement Learning
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.
Precise but Natural Specification for Robot Tasks
We present Flipper, a natural language interface for describing high-level task specifications for robots that are compiled into robot actions.
