Search Results for author: Gil Lederman

Found 5 papers, 2 papers with code

Demonstration Informed Specification Search

1 code implementation20 Dec 2021 Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman, Sanjit A. Seshia

To address this deficit, we propose Demonstration Informed Specification Search (DISS): a family of algorithms parameterized by black box access to (i) a maximum entropy planner and (ii) an algorithm for identifying concepts, e. g., automata, from labeled examples.

Learning Branching Heuristics for Propositional Model Counting

no code implementations7 Jul 2020 Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger Grosse, Edward Lee, Sanjit A. Seshia, Fahiem Bacchus

Propositional model counting or #SAT is the problem of computing the number of satisfying assignments of a Boolean formula and many discrete probabilistic inference problems can be translated into a model counting problem to be solved by #SAT solvers.

Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

no code implementations ICLR 2020 Gil Lederman, Markus Rabe, Sanjit Seshia, Edward A. Lee

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.

reinforcement-learning

Learning Heuristics for Automated Reasoning through Reinforcement Learning

no code implementations ICLR 2019 Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia

We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning.

reinforcement-learning

Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

1 code implementation20 Jul 2018 Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.

reinforcement-learning

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