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DC Field | Value | Language |
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dc.contributor.author | Zammit, Marvin | - |
dc.contributor.author | Liapis, Antonios | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.date.accessioned | 2024-04-26T10:05:32Z | - |
dc.date.available | 2024-04-26T10:05:32Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Zammit, M., Liapis, A., & Yannakakis, G. N. (2024). MAP-elites with transverse assessment for multimodal problems in creative domains. International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMusArt). Aberystwyth, Wales, UK | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121431 | - |
dc.description.abstract | The recent advances in language-based generative models have paved the way for the orchestration of multiple generators of different artefact types (text, image, audio, etc.) into one system. Presently, many open-source pre-trained models combine text with other modalities, thus enabling shared vector embeddings to be compared across different generators. Within this context we propose a novel approach to handle multimodal creative tasks using Quality Diversity evolution. Our contribution is a variation of the MAP-Elites algorithm, MAP-Elites with Transverse Assessment (MEliTA), which is tailored for multimodal creative tasks and leverages deep learned models that assess coherence across modalities. MEliTA decouples the artefacts’ modalities and promotes cross-pollination between elites. As a test bed for this algorithm, we generate text descriptions and cover images for a hypothetical video game and assign each artefact a unique modality-specific behavioural characteristic. Results indicate that MEliTA can improve text-to-image mappings within the solution space, compared to a baseline MAP-Elites algorithm that strictly treats each image-text pair as one solution. Our approach represents a significant step forward in multimodal bottom-up orchestration and lays the groundwork for more complex systems coordinating multimodal creative agents in the future. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Robotics | en_GB |
dc.subject | Evolutionary robotics | en_GB |
dc.subject | Genetic programming (Computer science) | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Computational intelligence | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Generative programming (Computer science) | en_GB |
dc.title | MAP-elites with transverse assessment for multimodal problems in creative domains | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMusArt) | en_GB |
dc.bibliographicCitation.conferenceplace | Aberystwyth, Wales, United Kingdom, 03-05/04/2024 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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map_elites_with_transverse_assessment_for_multimodal_problems_in_creative_domains.pdf | 9 MB | Adobe PDF | View/Open |
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