creativeEndvs1/5 Now in Artificial Life: our research on the creativity of unsupervised learning, grounded in creativity theory! Core finding: a simple model of attractor networks with Hebbian learning is sufficient to constitute a creative process, yielding creative products as solutions of the optimization. https://doi.org/10.1162/ARTL.a.10
2/5 Developed to model complex adaptive systems in ALife and advocated as a candidate for minimal agency, the Self-Optimization (SO) model can be considered as the 3rd operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance.
3/5 More specifically, we demonstrate that modifying the SO model learning parameters gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the creative potential of learning systems.
4/5 This work contributes to a young research agenda seeking to understand creativity beyond the realm of humans or highly developed animals. We argue for the SO model as a fascinating candidate to study the effect of learning on creativity from the bottom up - in life as it is and as it could be.