Passive Model Learning of Visibly Deterministic Context-free Grammars

TL;DR

This paper introduces PAPNI, a passive learning algorithm for visibly deterministic context-free grammars, extending RPNI to handle pushdown automata with known input alphabet decomposition, and demonstrates its effectiveness through empirical evaluation.

Contribution

PAPNI generalizes RPNI to learn deterministic context-free grammars modeled as visibly deterministic pushdown automata, assuming known alphabet decomposition.

Findings

PAPNI effectively learns deterministic context-free grammars from positive and negative samples.

The learned models show comparable or improved predictive accuracy over RPNI.

Empirical evaluation on literature-based grammars validates the approach.

Abstract

We present PAPNI, a passive automata learning algorithm capable of learning deterministic context-free grammars, which are modeled with visibly deterministic pushdown automata. PAPNI is a generalization of RPNI, a passive automata learning algorithm capable of learning regular languages from positive and negative samples. PAPNI uses RPNI as its underlying learning algorithm while assuming a priori knowledge of the visibly deterministic input alphabet, that is, the alphabet decomposition into symbols that push to the stack, pop from the stack, or do not affect the stack. In this paper, we show how passive learning of deterministic pushdown automata can be viewed as a preprocessing step of standard RPNI implementations. We evaluate the proposed approach on various deterministic context-free grammars found in the literature and compare the predictive accuracy of learned models with RPNI.