Learning Tree Automata with Term Rewriting

TL;DR

This paper introduces an enhanced tree automata learning algorithm that integrates deductive inference using term rewriting systems, significantly reducing query complexity for learning tree languages.

Contribution

It extends the Angluin-style learning algorithm for tree automata by incorporating term rewriting systems to improve efficiency and express natural properties of tree data.

Findings

Incorporating term rewriting reduces the number of queries needed.

Examples demonstrate natural properties of tree data can be expressed and exploited.

Significant query complexity reduction achieved in the learning process.

Abstract

We present an extension of the Angluin-style learning algorithm for tree automata that incorporates deductive inference. The learning algorithm is provided with a term rewriting system that specifies properties of the target tree language (e.g., the order of subtrees under a symbol f is irrelevant). This term rewriting system is used to infer answers to some queries, which reduces the query complexity of the learning algorithm. We present examples of rewrite systems that express natural properties of tree-structured data, which yield a significant reduction in the number of queries.