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Problems of origin, emergence, cognition, creativity, and novelty have always interested us. In the 13th century, Ramon Llull created a machine in the form of rotating circles from paper to generate new logical combinations, in which Llull himself saw the new knowledge (Bonner, 2007). Influenced by Llull’s work, Gottfried Wilhelm Leibniz had a particular dream: (a) to create a universal language that could describe all sorts of problems, and (b) find a decision-making method to solve all the problems mentioned in this language. He was also the first to propose the binary principle for machine data representation (Leibniz, 1679; Kluge, 1980). His idea became an important philosophical question: can one solve all the problems formulated in the universal language? This challenge became known in 1928 as a decision problem or Entscheidungsproblem, followed by David Hilbert (Hilbert & Ackermann, 1928). In 1936, Alonzo Church and Alan Turing independently solved it in a negative (Church, 1936a, 1936b; Turing, 1937). A bit later, Stanislaw Ulam and John von Neumann contributed to the theory of cellular automata (Mainzer & Chua, 2011). In 1967, Konrad Zuse revisited the problem of universality, suggesting that simple rules create both the common structures and the complexity (Zuse, 1967). This finding has been a theoretical basis for the newborn generative art, in which an algorithm, a set of instructions, forms the pattern with aesthetic meaning (Wolfram, 2002; Martínez & Adamatzky, 2016).
According to Roman Verostko, the tessellations in ancient Islamic art were created using algorithms (Verostko, 2002). An advanced style of algorithmic art is the geometrical patterns drawn by Maurits Cornelis Escher (Hofstadter, 1999). In the 1960s, electronics appeared as a new art medium. Georg Nees, Frieder Nake, Michael Noll, Manfred Mohr, and Vera Molnár created the earliest computer-generated algorithmic artworks. They used digital technology as a part of the creative process (Dreher, 2014; Taylor, 2014). Because the size and price of electronic components have dropped dramatically in recent decades, embedded electronics and physical computing have become ubiquitous (O’Sullivan & Igoe, 2004). Now the software runs not only on the mainframe, desktop and laptop computers, but also on a tablet PC, mobile phone, and on a smartwatch. For example, microcontrollers with sensors are programmed to work as interactive systems (Noble, 2009). Such sensors play an important role as a bridge between the physical and the digital worlds; they provide an interactivity of the system. The interactivity means a dialogue between the user and the computing system, or between the viewer and the artwork (Dannenberg & Bates, 1995; Muller & Edmonds, 2006; Jacucci et al., 2010; Edmonds, 2018). Because the art no longer focuses on the form of the object, but formulates the rules for creation and development of an art object, the theoretical and aesthetic impact of interactive art is deeper than expected. In the context of the history of interactive art and digital media (Balbi & Magaudda, 2018), there has been a discourse in relation to these practices over the last 30 years, e.g. the writings of Katja Kwastek (2013), Edward Shanken (2014), and Christiane Paul (2015).