Creativity and Machine Learning: A Survey GIORGIO FRANCESCHELLI, Alma Mater Studiorum Università di Bologna, Italy MIRCO MUSOLESI, University College London, United Kingdom, The Alan Turing Institute, United Kingdom, and Alma Mater Studiorum Università di Bologna, Italy There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field. 1 INTRODUCTION In 1842, Lady Lovelace, an English mathematician and writer recognized by many as the first computer programmer, wrote that the Analytical Engine - the digital programmable machine proposed by Charles Babbage [3] a hundred years before the Turing machine [114]- “has no pretensions to originate anything, since it can only do whatever we know how to order it to perform” [72]. This consideration, which Alan Turing referred to as “Lovelace’s objection” [115] was just the first fundamental meeting between computer science and creativity. In particular, the last thirty years of the Twentieth century have been marked by many attempts of building machines able to “originate something”. From the beginning of the Seventies of the past century with the AARON Project by Harold Cohen, a program designed to autonomously draw images provided by a domain knowledge and a knowledge of representational strategy [16] and the Computerized Haiku by Margaret Masterman1, we have witnessed several examples of applications of artificial intelligence to a variety of artistic fields. Examples include the storyteller TALE- SPIN [71], RACTER and its poems’ book [84], and MEXICA and its short narratives [82]. Applications were not limited to novels and paintings: BACON was used to simulate human thought processes and discover scientific laws61 [ ] and with the COPYCAT Project Douglas Hofstadter and Melanie Mitchell proposed a computer program to discover insightful analogies [46]. The themes have also been extensively examined by Douglas Hofstadter in the Pulitzer Prize Gödel, Escher, Bach: an Eternal Golden Braid [45], in which the author explains the idea of self-reference in the production of creative work and its implications for artificial intelligence2. In this context, we have witnessed the emergence of the Computational Creativity field [13]. We will adopt the definition by Colton and Wiggins [21], according to whom, Computational Creativity is the philosophy, science and engineering of behaviors that unbiased observers would deem to be creative. It is important to highlight the use of the terms “responsibilities” (so, not considering tools but really independent systems) and, of course, “unbiased observers” (so, without any kind of prejudice about what a machine can or cannot do; our hope is that the reader can be considered arXiv:2104.02726v2 [cs.LG] 20 Apr 2021 as an “unbiased observer”; if not, we will make, in any case, our best). Computational Creativity can also be defined as the study and support, through computational means and methods, of behaviors exhibited by natural and artificial systems, which would be deemed creative if exhibited by humans,as proposed by Wiggins [124]. In this work, the author studies the links between creativity models and search algorithms of AI from a theoretical perspective. The author shows that by understanding them in depth, it might be possible 1http://www.in-vacua.com/cgi-bin/haiku.pl 2Quite interestingly, after Hofstadter’s classic, many AI projects focused on Bach’s music, from Bach by Design, the first recording produced by the artificial composer developed by David Cope [22] to COCONET [48] and DeepBach [44]. 1 Franceschelli and Musolesi to design solutions for both exploratory and transformational creativity. The definition of exploratory creativity and transformational creativity is provided by Margaret Boden in [8], another seminal work about Computational Creativity. Boden identifies three forms of creativity: combinatorial, exploratory and transformational creativity. Combinatorial creativity is about making unfamiliar combinations of familiar ideas; the definition of the other two, instead, is based on conceptual spaces, i.e., structured styles of thought (any disciplined way of thinking that is familiar to a certain culture or peer group): exploratory creativity involves the exploration of the structured conceptual space, while transformational creativity involves changing the conceptual space in a way that new thoughts, previously inconceivable, become possible. This is not the only interesting definition Boden provides. In fact, Boden also states that “creativity isthe ability to generate ideas or artifacts that are new, surprising and valuable”, where three sorts of surprise are considered. The first is the one that goes against statistics; the second is the one when you had not realized that this particularidea was part of it; and the third is when the idea is (or, better, was) apparently impossible. We will consider this definition, that we will call Boden’s three criteria, as one of the cornerstones of the rest of the research. Finally, Boden provides one last definition, linked with the very beginning of this Introduction. In fact, Boden states that, when talking about Computational Creativity, it is possible to distinguish four different questions, called as Lovelace questions, because many people would respond to them by using the argument cited above. The first Lovelace question is whether computational ideas can help us understand how human creativity is possible; the second is whether computers could ever do things which at least appear to be creative; the third is whether a computer could ever appear to recognize creativity; and the fourth is whether computer themselves could ever really be creative (the originality of the products can be attributed only to the machine) [8]. The first one is studied in Boden’s work in detail; from now on, after discussing the scope of the survey, we will provide the reader with an overview of the techniques that can answer second question and, possibly, generate insights about how machine learning can be used to measure creativity, trying to answer the third question. Before starting the description of techniques for generation and evaluation, we need to explain why this survey is not about artificial intelligence in general, defined as the design and implementation of intelligent agents that receive perception information from the environment and take actions that affect that environment (according to the definition contained in [95], which is shared by many researchers in this field). The survey focuses instead on machine learning, which is a subset of artificial intelligence defined as the study of computer algorithms that improve automatically through experience [74]. In order to consider a machine as potentially creative, there is the need of assigning more and more responsibilities to the generative system [21], and machine learning seems more appropriate since it avoids the need of a human behind it to specify all the required knowledge [35]; as pointed out in [17], the use of evolutionary or machine learning methods makes the behavior of the software unpredictable for the programmer, who is, in a sense, left out from the generative process. The fact that artificial intelligence and machine learning will be the enabling technologies for supporting creativity has been apparent since Turing’s seminal work. We can find this intuition in particular in the section “Learning Machines” [115] in which Turing, trying to find a way to overcome Lovelace’s objection, suggested that probably the best possible way to simulate human mind and pass the Turing Test is not to produce a program, but instead to simulate a child’s mind and then make it grows by learning. The relevance of this kind of systems, and in particular of neural networks, in creativity study is well-explained also by Boden, who notices that connectionist ideas of neural networks can help answer the first Lovelace question and might answer (and maybe have already answered) the second question, also suggesting that, by using them, computers might recognize certain aspects of creativity (answering the third question) [8]. It is worth noting that, according to some, computers not only might do things which appear to be creative, but already are expressing some forms of creativity (see, for example Goodfellow’s 2 Creativity and Machine Learning: A Survey interview by [73], where he said that “if creativity means producing something new and beneficial, machine learning algorithms are already at that point”). Following these considerations, to the best of our knowledge, this survey is the first to explore if and how machine learning algorithms have been used as a basis for (computational) creativity in both the generation and evaluation paths. Instead, for a reader interested in a survey concerning AI and creativity in general, we recommend [93]; for a more technical view of generative deep learning and for a view of other related techniques (for example, style transfer or machine translation), we suggest [30]; finally, for a view of artistic AI works (also in human-computer co-creations [43]), [73] is a very comprehensive source of information. 2 GENERATIVE