GENERALIZE: A framework for evolving searching constraints for domain-specific languages in program synthesis

GENERALIZE: A framework for evolving searching constraints for domain-specific languages in program synthesis

Kroes, Lucas (TU Delft Electrical Engineering, Mathematics and Computer Science)

Dumančić, S. (mentor)
Smaragdakis, G. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-18

In this paper, we propose a method for eliciting constraints for arbitrary Domain-Specific Languages (DSL) in Program Synthesis search. We argue that we can successfully predict constraints using a form of attribute-based induction. We also provide a novel approach to constraint verification using genetic algorithms to optimize desired results. We implement our approach into GENERALIZE, a novel algorithm for reducing DSL size. GENERALIZE is tested and compared against the default Brute algorithm using 2 different program synthesis domains, robot planning and pixel art. These experiments show that GENERALIZE does not improve performance if good objective functions are available, because of a tendency to get stuck in local heuristic minima. It can increase performance if no such function is available.

Program Synthesis
Constraint eliciation
Observational Method

http://resolver.tudelft.nl/uuid:aa9f379e-f10e-4c52-ac0d-127e1a492b47

https://github.com/FabianRadomski/EvolvingProgramSynthesisers Code base link

Student theses

bachelor thesis

© 2022 Lucas Kroes