An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

TL;DR

This paper introduces a multi-agent machine learning system using radial basis neural networks and reinforcement learning for authorship attribution, emphasizing semantic analysis and adaptability across contexts.

Contribution

It presents a novel multi-agent approach combining semantic analysis with RBPNN and reinforcement learning for improved authorship attribution.

Findings

Effective in diverse lexical domains

Capable of continuous self-adjustment

Generalizable semantic analysis method

Abstract

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.