1 code implementation • 20 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.
no code implementations • 7 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.
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.
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.
1 code implementation • 20 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.