Random Words in a (Weighted) Regular Language: a Free Energy Approach
Cewei Cui, Zhe Dang
TL;DR
This paper explores the generation of random words in weighted regular languages that maximize free energy, applying thermodynamics formalism to improve applications in security and software engineering.
Contribution
It introduces a novel approach using free energy and thermodynamics formalism to generate typical words in weighted regular languages.
Findings
Identifies words with maximal free energy in weighted regular languages.
Demonstrates applications in anomaly detection and test case generation.
Provides a new theoretical framework for analyzing regular languages.
Abstract
We study random words in a weighted regular language that achieve the maximal free energy using thermodynamics formalism. In particular, typical words in the language are algorithmically generated which have applications in computer security (anomaly detection) and software enegineering (test case generation).
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Algorithms and Data Compression · semigroups and automata theory