Swarm Intelligence and Weak Artificial Creativity

Swarm intelligence via its infamous struggle to identify a suitable balance between exploration and exploitation phases, provides a valuable mean to approach artificial creativity. This work deploys two swarm intelligence algorithms, one simulating the behaviour of birds flocking and fish schooling (Particle Swarm Optimisation) and the other mimicking the behaviour of ants foraging (Stochastic Diffusion Search) in order to lay the foundation for a discussion addressing the concepts of freedom and constraint within the topic of creativity in general, and more specifically their impact on the artificial creativity of the underlying systems. An analogy is drawn on mapping these two `prerequisites' of creativity onto the two well-known aforementioned phases of exploration and exploitation in swarm intelligence algorithms. This is accompanied by the visualisation of the behaviour of the swarms whose performance are evaluated in the context of the arguments presented. Additionally in the spirit of Searle's definition of weak and strong artificial intelligence, a discussion on weak vs. strong artificial creativity in swarm intelligence systems is presented.