Visual creativity through concept combination using quantum cognitive models

• Published in: Scientia Iranica. Transaction D, Computer science & engineering, electrical engineering Vol. 31; no. 2; pp. 118 - 135
• Main Authors: Tabriz, M Ahrabi, Atani, T Rafiei, Ashtiani, M, Jahed-Motlagh, M R
• Format: Journal Article
• Language: English
• Published: Tehran Sharif University of Technology 01.04.2024
• Subjects: Agents (artificial intelligence); Artificial intelligence; Cognition; Cognition & reasoning; Cognitive ability; Cognitive models; Computational neuroscience; Context; Creativity; Image quality; Incompatibility; Information retrieval; Mathematics; Memory; Modelling; Semantics
• DOI: 10.24200/sci.2023.61494.7340

Computational creativity modeling, including concept combination, enables us to foster deeper abilities of Artificial Intelligence (AI) agents. Although concept combination has been addressed in a lot of computational creativity studies, findings show incompatibility amongst empirical data of concept combination and the results of the used methods. In addition, even though recent neuroscientific studies show the crucial impact of retrieving concepts' relations explicitly stored in episodic memory, it has been underestimated in modeling creative processes. In this paper, a quantum cognition-based approach is used to consider the context more effectively and resolve logical inconsistencies. Also, episodic memory is leveraged as the basis for the concept combination modeling process based on the created context. The result of the proposed process is a set of meaningful concepts and expressions as a combination of stimuli and related episodes, which are used to depict a visual collage as an image. The significant improvement in the quality of results in comparison with the existing methods suggests that quantum-like modeling can be considered the foundation for developing AI agents capable of creating artistic images or assisting a person during a creative process.