Computational Creativity Research Research Articles

A framework for extending co-creative communication models to sustainability research.

The UN Sustainable Development Goals (SDGs) present a challenge due to their potential for conflicting objectives, which hinders their effective implementation. In order to address the complexity of sustainability issues, a framework capable of capturing the specificity of diverse sustainability issues while offering a common methodology applicable across contexts is required. Co-creative communication can be regarded as a key source of uncertainty within functional systems, as it can be instrumental in realizing and sustaining sustainability. In this regard, the studies in Constructive approaches to Co-creative Communication (CCC), particularly those employing artificial intelligence (AI) methodologies such as computational social science and innovation studies, hold significant value for both theoretical and applied sustainability research. However, existing CCC frameworks cannot be directly applied to sustainability research. This work bridges this gap by proposing a framework that outlines a general approach to establishing formalized definitions of sustainability from the lens of communication. This approach enables the direct application of CCC models to sustainability studies. The framework is based on systems theory and the methodologies of artificial intelligence, including computational/symbolic modeling and formal methods. This framework emphasizes the social function of co-creative communication and the interaction between the innovation process and the sustainability of the system. It can be concluded that the application of our framework enables the achievements of CCC to be directly applied to sustainability research. Researchers from different disciplines are therefore able to establish their own specific definitions of sustainability, which are tailored to their particular concerns. Our framework lays the groundwork for future sustainability studies that employs CCC, facilitating the integration of CCC insights into sustainability research and application. The outcomes of computational creativity research based on AI technologies, such as distributed artificial intelligence and self-organizing networks, can deepen the understanding of sustainability mechanisms and drive their practical applications. Furthermore, the functional role of co-creative communication in societal sustainability proposed in this work offers a novel perspective for future discussions on the evolutionary adaptation of co-creative communication.

CCDSF: A Computational Creative Design Systems Framework

From a mixed perspective of the fields of computational creativity and design research, we present a formal framework called Computational Creative Design Systems Framework (CCDSF) for describing, analyzing, and modeling Computational Creative Design Systems (CCDSs), either as autonomous agents or as collaborative systems integrating different agents, including both pure computational and human–computer scenarios. The CCDSF extends and unifies three existing frameworks: (i) the Function–Behavior–Structure framework for designing, to integrate design domain-specific knowledge; (ii) the Creative Systems Framework, to model design as a creative process; and (iii) the Conceptual Spaces Theory framework, to define the CCDSF at a more neutral level to be compatible with both the conceptual-space nature of the creative process and the implementation-level representation systems. The CCDSF core is a layered schema that structures the activity of designing through design concepts produced by design processes instrumented by agent-specific perception and action strategies, within a hierarchy of what we call design levels. Each layer of the schema constitutes a creative system that can implement exploratory, combinational, and transformational creativity mechanisms, including the possibility of defining the system at a meta-creative level. The hierarchical arrangement of creative systems defines the architecture of a CCDS. This architecture enables modeling CCDSs considering both the perspective of the generation and evaluation of artifacts that could be deemed creative and the precise modeling of the cognitive process of design, emphasizing its situated nature. To illustrate its usefulness, in the last part of this paper, we use the proposed framework to describe a real system in the videogame design domain.

Evaluating Computational Creativity

This article is a tutorial for researchers who are designing software to perform a creative task and want to evaluate their system using interdisciplinary theories of creativity. Researchers who study human creativity have a great deal to offer computational creativity. We summarize perspectives from psychology, philosophy, cognitive science, and computer science as to how creativity can be measured both in humans and in computers. We survey how these perspectives have been used in computational creativity research and make recommendations for how they should be used.

Has computational creativity successfully made it “Beyond the Fence” in musical theatre?

ABSTRACTA significant test for software is to task it with replicating human performance, as done recently with creative software and the commercial project Beyond the Fence (undertaken for a television documentary Computer Says Show). The remit of this project was to use computer software as much as possible to produce “the world's first computer-generated musical”. Several creative systems were used to generate this musical, which was performed in London's West End in 2016. This paper considers the challenge of evaluating this project. Current computational creativity evaluation methods are ill-suited to evaluating projects that involve creative input from multiple systems and people. Following recent inspiration within computational creativity research from interaction design, here the DECIDE evaluation framework is applied to evaluate the Beyond the Fence project. Evaluation finds that the project was reasonably successful at achieving the task of using computational generation to produce a credible musical. Lessons have been learned for future computational creativity projects though, particularly for affording creative software more agency and enabling software to interact with other creative partners. Upon reflection, the DECIDE framework emerges as a useful evaluation “checklist” (if not a tangible operational methodology) for evaluating multiple creative systems participating in a creative task.

Two analogy strategies: the cases of mind metaphors and introspection

ABSTRACTAnalogical reasoning is often employed in problem-solving and metaphor interpretation. This paper submits that, as a default, analogical reasoning addressing these different tasks employs different mapping strategies. In problem-solving, it employs analogy-maximising strategies (like structure mapping, Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56); in metaphor interpretation, analogy-minimising strategies (like ATT-Meta, Barnden, J. A. (2015). Open-ended elaborations in creative metaphor. In T. R. Besold, M. Schorlemmer, & A. Smaill (Eds.), Computational creativity research: Towards creative machines (pp. 217–242). Berlin: Springer). The two strategies interact in analogical reasoning with conceptual metaphors. This interaction leads to predictable fallacies. The paper supports these hypotheses through case-studies on “mind” metaphors from ordinary discourse, and abstract problem-solving in the philosophy of mind, respectively. It shows that (1) default metaphorical interpretations for vision- and space-cognition metaphors can be derived with a variant of the analogy-minimising ATT-Meta approach, (2) philosophically influential introspective conceptions of the mind can be derived with conceptual metaphors only through an analogy-maximising strategy, and (3) the interaction of these strategies leads to hitherto unrecognised fallacies in analogical reasoning with metaphors. This yields a debunking explanation of introspective conceptions.

Data mining and machine learning in computational creativity

Creative machines are an old idea, but only recently computational creativity has established itself as a research field with its own identity and research agenda. The goal of computational creativity research is to model, simulate, or enhance creativity using computational methods. Data mining and machine learning can be used in a number of ways to help computers learn how to be creative, such as learning to generate new artifacts or to evaluate various qualities of newly generated artifacts. In this review paper, we give an overview of research in computational creativity with a focus on the roles that data mining and machine learning have had and could have in creative systems. WIREs Data Mining Knowl Discov 2015, 5:265–275. doi: 10.1002/widm.1170This article is categorized under: Application Areas > Science and Technology Application Areas > Society and Culture

Computational Social Creativity.

This article reviews the development of computational models of creativity where social interactions are central. We refer to this area as computational social creativity. Its context is described, including the broader study of creativity, the computational modeling of other social phenomena, and computational models of individual creativity. Computational modeling has been applied to a number of areas of social creativity and has the potential to contribute to our understanding of creativity. A number of requirements for computational models of social creativity are common in artificial life and computational social science simulations. Three key themes are identified: (1) computational social creativity research has a critical role to play in understanding creativity as a social phenomenon and advancing computational creativity by making clear epistemological contributions in ways that would be challenging for other approaches; (2) the methodologies developed in artificial life and computational social science carry over directly to computational social creativity; and (3) the combination of computational social creativity with individual models of creativity presents significant opportunities and poses interesting challenges for the development of integrated models of creativity that have yet to be realized.

Applying computational creativity to health and social care

Computational creativity is an inter-disciplinary field that seeks to simulate and replicate creative processes to generate novel and useful outcomes using a computer in order to enhance human creativity. Most computational creativity research has been applied to artistic domains - music, poetry and visual art. This submission reports research that applied computational creativity algorithms in a set of mobile software apps to support the care of older people with dementia.. Each app was developed to support nurses and carers to deliver more effective person-centred care by being more creative and more reflective about the care that is delivered. One app in the set implements new computational creativity algorithms to generate support for a carer or nurse to create new strategies with which to manage challenging behaviours (e.g. verbal aggression, refusing food, violence) exhibited by residents, using information about successful care strategies. A second one implements new computational creativity algorithms to generate support for a carer or nurse to explore and create new forms of care activities, using digital information about the life and activities of each resident. A third app implements reflective learning strategies that a carer and nurse can use to make sense of and learn creatively about each resident’s challenging behaviours as recorded digitally in daily care notes. Summative evaluations of some of these apps reported previously have demonstrated the effectiveness of app use to resolve instances of challenging behaviour in older people with dementia. This presentation will make the case for the use of computational creativity algorithms in health and social care, present the different algorithms that have been developed, and demonstrate the potential for their wider uptake in the sector.