The maple series of sat solvers is a family of conflict driven clause learning sat solvers outfitted with machine learning based heuristics. To instantiate quanti ers they rely on heuristic techniques that generate thousands of instances most of them useless.
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Up to 100x speed up over z3 solver.
Smt solver machine learning. Smt solvers are among the most suited tools for quanti er free rst order problems and their support for quanti ed formulas has been improving in recent years. Learning the task of learning smtlra formulas from examples of feasible and infeasible instances and we contributeincal an exact non greedy al gorithm for this setting. The problem of finding a strategy for solving an smt formula is formulated as a reinforcement learning problem where the cost function is essentially runtime. 1the design and implementation of our portfolio solver where4 which uses supervised machine learning to predict the best solver to use based on metrics collected from goals. Augment state of the art smt solver with the learned strategy. Machine learning synthesis. The synthesis tools im most familiar with work by solving logical constraints using an smt solver. Input language to develop a portfolio smt solver specically designed for software verication. The main contributions of this paper are. We propose to apply state of the art machine learning techniques as. This paper studies machine learning techniques to learn heuristics that help guide smt solvers in particular z3 to speed up finding solutions. Cav 2017 joint work with clark barrett david dill kyle julian and mykel kochenderfer. Input language to develop a portfolio smt solver specically designed for software verication. 2 30 based on. Incal is an incremental algorithm that achieves.
Augment your smt solver by learning to optimize its performance for your dataset of formulas. Currently maplesat supports machine learning based branching and restarts policies. The main contributions of this paper are. 1the design and implementation of our portfolio solver where4 which uses supervised machine learning to predict the best solver to use based on metrics collected from goals. An efficient smt solver for. Our approach encodes the learning task itself as an smtlra satisability problem that can be solved directly by smt solvers. Nasa machine learning workshop august 302017. Machine learning using program synthesis. An efficient smt solver for verifying deep neural networks in proc. A machine learning based sat solver. Learn strategy that is fast at solving formulas in your dataset. Machine learning based solver design cdcl sat solvers are polynomially equivalent to merge resolution proof complexity of smt solversrkg18 introduced the merge parameter as a basis for upper bound analysis zg18 merge as a feature for machine learning based clause deletion introduced the idea of solver as a collection of. In the future we plan to add a machine learning based clause learning policy. To do machine learning using program synthesis were going to encode a machine learning problem as a synthesis problem and solve the resulting logical constraints.
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