Neural-guidance for symbolic reasoning
Symbolic reasoning begot Artificial Intelligence (AI). With the recent advances in Deep Learning, many traditional AI areas such as Computer Vision and Natural Language Processing have moved to probabilistic-based approaches. However, in applications where there is little to no room for uncertainty, such as Compiler or Software verification, symbolic reasoning is still the go-to option. In this thesis, we bring the advantage of data-driven learnable models into the precise world of symbolic reasoning. In particular, we choose to tackle two specific problems: Model Checking, in the context of Inductive Generalization, and Compiler Optimization, in the context of Software Debloating. We implemented our approach in two tools, named Dopey and DeepOccam, respectively. They both use traces generated from running a task to learn a better heuristic, and use said heuristic to improve subsequent runs of the same or similar tasks. Our results show that both neural-based heuristics outperform handcrafted heuristics.
Cite this version of the work
Le Nham (2020). Neural-guidance for symbolic reasoning. UWSpace. http://hdl.handle.net/10012/16444