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Found 5 papers, 2 papers with code
Demonstration Informed Specification Search
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
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
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
Learning Heuristics for Automated Reasoning through Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning.
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
