Collaborating Authors

Evaluating Creativity in Computational Co-Creative Systems Artificial Intelligence

This paper provides a framework for evaluating creativity in co-creative systems: those that involve computer programs collaborating with human users on creative tasks. We situate co-creative systems within a broader context of computational creativity and explain the unique qualities of these systems. We present four main questions that can guide evaluation in co-creative systems: Who is evaluating the creativity, what is being evaluated, when does evaluation occur and how the evaluation is performed. These questions provide a framework for comparing how existing co-creative systems evaluate creativity, and we apply them to examples of co-creative systems in art, humor, games and robotics. We conclude that existing co-creative systems tend to focus on evaluating the user experience. Adopting evaluation methods from autonomous creative systems may lead to co-creative systems that are self-aware and intentional.

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach Artificial Intelligence

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System Machine Learning

This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching canvas. The AI agent responds to the human designer's sketch with a new sketch that is a conceptual shift: intentionally varying the visual and conceptual similarity with increasingly more novelty. The paper presents the results of a user study showing that increasing novelty in the AI contribution is associated with higher creative outcomes, whereas low novelty leads to less creative outcomes.


International Business Times

A total of 155 people took part in the study and the researchers also found that happy music gave a boost to divergent thinking. "Creativity was higher for participants who listened to'happy music' (i.e., classical music high on arousal and positive mood) while performing the divergent creativity task, than for participants who performed the task in silence. No effect of music was found for convergent creativity," highlights the research paper. In the research titled, "The sound of cooperation: Musical influences on cooperative behavior," lead author Kevin Kniffin, a behavioral scientist, highlighted how employees could be more productive if they listened to happy music.

Creative men are as attractive to women as handsome men

Daily Mail - Science & tech

Men who are not blessed with matinee idol good looks can still attract the opposite sex - if they are creative, a study suggests. It seems having a way with words, talent with a paintbrush or the ability to strum a guitar gives the impression a man has artistic skills. And an experiment has found that a creative fellow whose face is rather plain, is just as attractive to women as a handsome man who is not creative. Men who are not blessed with matinee idol good looks can still attract the opposite sex - if they are creative, a study suggests. Researchers carried out a series of tests to see what effect being creative had on perceptions of attractiveness.