HAMLET: A Hierarchical Agent-based Machine Learning Platform

TL;DR

HAMLET is a novel hierarchical multi-agent platform that models distributed machine learning systems as hypergraphs, enabling flexible, scalable, and analyzable solutions without algorithm or dataset restrictions.

Contribution

It introduces a hybrid, hierarchical agent-based platform for machine learning that is theoretically sound, complete, and empirically validated on diverse tasks and datasets.

Findings

Platform is sound and complete with polynomial complexity.

Empirical validation on 120 training and 4 testing tasks.

Demonstrates flexibility and analytical capabilities for ML research.

Abstract

Hierarchical Multi-Agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this paper, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research and democratization of geographically and/or locally distributed machine learning entities. The proposed system models a machine learning solutions as a hypergraph and autonomously sets up a multi-level structure of heterogeneous agents based on their innate capabilities and learned skills. HAMLET aids the design and management of machine learning systems and provides analytical capabilities for research communities to assess the existing and/or new algorithms/datasets through flexible and customizable queries. The proposed hybrid machine learning platform does not assume restrictions on the type of learning algorithms/datasets and is theoretically proven to be sound and complete with polynomial computational requirements. Additionally, it is examined empirically on 120 training and four generalized batch testing tasks performed on 24 machine learning algorithms and 9 standard datasets. The provided experimental results not only establish confidence in the platform's consistency and correctness but also demonstrate its testing and analytical capacity.