Synthesizing DFAs using RNNs

Overview

I implemented an RNN-based model to tackle the fascinating problem of automatically synthesizing Deterministic Finite Automata (DFAs). This project explored how neural networks can learn to represent and generate formal languages, essentially teaching machines to understand the underlying patterns and rules that define computational languages.

Technical Approach

Model Development

Architecture: Built a custom RNN model in Python designed specifically for sequence learning and pattern recognition
Target Problem: Focused on automating DFA synthesis, which traditionally requires manual rule specification
Language Representation: Developed methods for the model to learn and represent formal language structures

What I Worked On

Sequence Learning: Trained the RNN to understand patterns in formal language sequences
DFA Generation: Implemented algorithms to convert learned patterns into proper DFA structures
Pattern Recognition: Developed techniques for the model to identify and generalize language rules
Validation: Created methods to verify that generated DFAs correctly represent the intended languages

Results

The project successfully demonstrated that RNNs can:

Learn complex patterns in formal language sequences
Automatically generate valid Deterministic Finite Automata
Represent formal languages without explicit rule programming
Bridge the gap between neural networks and theoretical computer science concepts

Why This Matters

This work sits at the intersection of machine learning and theoretical computer science. Understanding how neural networks can learn formal language representations has implications for compiler design, natural language processing, and automated reasoning systems. It shows how AI can be applied to traditionally rule-based computational problems.

Project Duration: April 2021 – May 2021