[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116829-en":3,"doc-seo-116829-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116829,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Creativity and Machine Learning: a Survey - Overview","The survey reviews the history and current state of computational creativity, connecting creativity theories with machine learning methods, including generative deep learning, and associated automatic evaluation approaches. It discusses key contributions that shaped the field, explains foundational ideas such as Lovelace’s objection and the emergence of computational creativity, and introduces major creativity notions including exploratory and transformational creativity. The work concludes by identifying research challenges and emerging opportunities for future investigation.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \n[provided by](provided by arXiv.org)[ arXiv.org](provided by arXiv.org) e-Print Archive  \narXiv :2104 .02726v 1 [ cs .LG] 6 Apr 2021  \nCreativity and Machine Learning: a Survey  \nGIORGIO FRANCESCHELLI, Alma Mater Studiorum Università di Bologna, Italy  \nMIRCO MUSOLESI, University College London, United Kingdom, The Alan Turing Institute, United Kingdom, and Alma Mater Studiorum Università di Bologna, Italy  \nThere is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state ofthe 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.  \n1 INTRODUCTION  \nIn 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 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.  \nIn 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 TALESPIN [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 laws [61] 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 .  \nIn 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 as an “unbiased observer”; if not, we will make, in any case, our best) .  \nComputational 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  \n1[http://www.in-vacua.com/cgi-bin/haiku.pl](http://www.in-vacua.com/cgi-bin/haiku.pl)  \n2 Quite ","cbCaifDWYqP58Tds","https://ap.wps.com/l/cbCaifDWYqP58Tds","pdf",887976,1,25,"English","en",105,"# Introduction\n## Foundations of computational creativity\n## Definitions and creativity criteria\n## Links to AI search and opportunities","[{\"question\":\"What does the survey focus on?\",\"answer\":\"It presents an overview of computational creativity’s history and state of the art, linking creativity theories with machine learning techniques, including generative deep learning, and automatic evaluation methods.\"},{\"question\":\"How is computational creativity defined in the survey?\",\"answer\":\"The survey adopts definitions by Colton and Wiggins and also by Wiggins, framing computational creativity as the study and engineering of behaviors considered creative by unbiased observers.\"},{\"question\":\"What creativity types and criteria are highlighted?\",\"answer\":\"It describes exploratory and transformational creativity using Margaret Boden’s concepts, and it uses Boden’s criteria for creativity based on novelty, surprisingness, and value.\"}]","Creativity and Machine Learning: a Survey - 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