Ontology-Driven Multi-Agent System for Cross-Domain Art Translation

Viktor Matanski, Anton Iliev (), Nikolay Kyurkchiev () and Todorka Terzieva
Additional contact information
Viktor Matanski: Department of Computer Technologies, University of Plovdiv “Paisii Hilendarski”, 4000 Plovdiv, Bulgaria
Anton Iliev: Department of Computer Technologies, University of Plovdiv “Paisii Hilendarski”, 4000 Plovdiv, Bulgaria
Nikolay Kyurkchiev: Department of Computer Technologies, University of Plovdiv “Paisii Hilendarski”, 4000 Plovdiv, Bulgaria
Todorka Terzieva: Department of Computer Technologies, University of Plovdiv “Paisii Hilendarski”, 4000 Plovdiv, Bulgaria

Future Internet, 2025, vol. 17, issue 11, 1-33

Abstract: Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a novel approach combining a multi-agent system (MAS) architecture with an ontology-guided semantic representation to achieve cross-domain art translation while preserving the original artwork’s meaning and emotional impact. In our concept, specialized agents decompose the task: a Perception Agent extracts symbolic descriptors from the source artwork, a Translation Agent maps these descriptors using shared knowledge base, a Generator Agent creates the target-modality artwork, and a Curator Agent evaluates and refines the output for coherence and style alignment. This modular design, inspired by human creative workflows, allows complex artistic concepts (themes, moods, motifs) to carry over across modalities in a consistent and interpretable way. We implemented a prototype supporting translations between painting and poetry, leveraging state-of-the-art generative models. Preliminary results indicate that our ontology-driven MAS produces cross-domain translations that preserve key semantic elements and affective tone of the input, offering a new path toward explainable and controllable creative AI. Finally, we discuss a case study and potential applications from educational tools to synesthetic VR experiences and outline future research directions for enhancing the realm of intelligent agents.

Keywords: multi-agent systems; intelligent agents; computational creativity; cross-domain generation; ontologies; human-AI collaboration; synesthesia (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/11/517/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/11/517/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:11:p:517-:d:1793235

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().