Conversational AutoML with Multi-Agent Architecture and Monte Carlo Tree Search for Hyperparameter Optimization

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dc.abstract.enAutomated Machine Learning (AutoML) reduces the technical requirements for applying machine learning, yet existing systems still demand technical expertise, optimize inefficiently, and rarely support conversational use. We present a conversational AutoML system with a five-agent architecture coordinated via natural language, tuning Random Forest and gradient-boosting models (XGBoost, LightGBM, and CatBoost) with Monte Carlo Tree Search (MCTS) and Bayesian optimization under repeated cross-validation conditions. Its distinctive component is an ensemble coordinator: specialist optimizer agents tune model families concurrently, and a coordinator fuses their outputs with the strongest baseline into a stacking ensemble, deploying it only when it improves on the best single model by cross-validation. This separates two measurable benefits: a speed-up from parallel search and an accuracy gain from ensemble fusion. On six benchmark datasets (five classification and one regression), the multi-agent coordinator is best or tied on every dataset and the only configuration with a positive average gain over defaults (+0.20 pp, versus −0.69 pp for single-agent MCTS); its search is 2.3× faster than the single-agent Bayesian baseline in aggregate, and it is systematically better than single-agent MCTS across datasets (Wilcoxon signed-rank, p = 0.031). While individual dataset gains are observed, none reach statistical significance when adjusted for multiple comparisons. For classification tasks, we additionally report F1-score, precision, recall, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve).
dc.affiliationWydział Projektowania w Warszawie
dc.contributor.authorGhafarian Mabhoot, Toktam
dc.contributor.authorQavi, Amirhossein
dc.date.access2026-09-23
dc.date.accessioned2026-10-13T11:09:57Z
dc.date.available2026-10-13T11:09:57Z
dc.date.created2026-09-21
dc.date.issued2026-09-23
dc.description.abstract<jats:p>Automated Machine Learning (AutoML) reduces the technical requirements for applying machine learning, yet existing systems still demand technical expertise, optimize inefficiently, and rarely support conversational use. We present a conversational AutoML system with a five-agent architecture coordinated via natural language, tuning Random Forest and gradient-boosting models (XGBoost, LightGBM, and CatBoost) with Monte Carlo Tree Search (MCTS) and Bayesian optimization under repeated cross-validation conditions. Its distinctive component is an ensemble coordinator: specialist optimizer agents tune model families concurrently, and a coordinator fuses their outputs with the strongest baseline into a stacking ensemble, deploying it only when it improves on the best single model by cross-validation. This separates two measurable benefits: a speed-up from parallel search and an accuracy gain from ensemble fusion. On six benchmark datasets (five classification and one regression), the multi-agent coordinator is best or tied on every dataset and the only configuration with a positive average gain over defaults (+0.20 pp, versus −0.69 pp for single-agent MCTS); its search is 2.3× faster than the single-agent Bayesian baseline in aggregate, and it is systematically better than single-agent MCTS across datasets (Wilcoxon signed-rank, p = 0.031). While individual dataset gains are observed, none reach statistical significance when adjusted for multiple comparisons. For classification tasks, we additionally report F1-score, precision, recall, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve).</jats:p>
dc.description.accesstimeat_publication
dc.description.issue19
dc.description.physical1-34
dc.description.sdgIndustryInnovationAndInfrastructure
dc.description.versionfinal_published
dc.description.volume15
dc.identifier.doi10.3390/electronics15194379
dc.identifier.issn2079-9292
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/2608
dc.identifier.weblinkhttps://www.mdpi.com/2079-9292/15/19/4379
dc.languageen
dc.pbn.affiliationinformatyka
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOTHER
dc.subject.enAutoML
dc.subject.enmulti-agent systems
dc.subject.enconversational AI
dc.subject.enhyperparameter optimization
dc.subject.enlarge language models
dc.subject.enensemble methods
dc.subject.engradient boosting
dc.subject.enMonte Carlo Tree Search
dc.subject.enBayesian optimization
dc.subject.encross-validation
dc.subject.enablation study
dc.swps.sciencecloudsend
dc.titleConversational AutoML with Multi-Agent Architecture and Monte Carlo Tree Search for Hyperparameter Optimization
dc.title.journalElectronics
dc.typeJournalArticle
dspace.entity.typeArticle