Data mining and machine learning in computational creativity

Creative machines are an old idea, but only recently computational creativity has established itself as a research field with its own identity and research agenda. The goal of computational creativity research is to model, simulate, or enhance creativity using computational methods. Data mining and machine learning can be used in a number of ways to help computers learn how to be creative, such as learning to generate new artifacts or to evaluate various qualities of newly generated artifacts. In this review paper, we give an overview of research in computational creativity with a focus on the roles that data mining and machine learning have had and could have in creative systems. WIREs Data Mining Knowl Discov 2015, 5:265–275. doi: 10.1002/widm.1170

[1]  Tony R. Martinez,et al.  Automatic Generation of Music for Inducing Emotive Response , 2010, ICCC.

[2]  Josep Blat,et al.  Generating Apt Metaphor Ideas for Pictorial Advertisements , 2013, ICCC.

[3]  Hannu Toivonen,et al.  Lexical Creativity from Word Associations , 2012, 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems.

[4]  Nada Lavrac,et al.  Bisociative Knowledge Discovery for Microarray Data Analysis , 2010, ICCC.

[5]  Douglas B. Lenat,et al.  AM, an artificial intelligence approach to discovery in mathematics as heuristic search , 1976 .

[6]  Ruli Manurung,et al.  Pemuisi: a constraint satisfaction-based generator of topical Indonesian poetry , 2014, ICCC.

[7]  A. Cardoso Creativity and Surprise , 2000 .

[8]  Raquel Hervás,et al.  Adapting a Generic Platform for Poetry Generation to Produce Spanish Poems , 2014, ICCC.

[9]  Nada Lavrac,et al.  Cross-domain literature mining: Finding bridging concepts with CrossBee , 2012, ICCC.

[10]  T. D. Bie,et al.  Hit Song Science Once Again a Science? , 2011 .

[11]  Graeme Ritchie,et al.  Some Empirical Criteria for Attributing Creativity to a Computer Program , 2007, Minds and Machines.

[12]  Arne Eigenfeldt,et al.  Considering Vertical and Horizontal Context in Corpus-based Generative Electronic Dance Music , 2013, ICCC.

[13]  Tony Veale,et al.  Creative introspection and knowledge acquisition: learning about the world through introspective questions and exploratory metaphors , 2011, AAAI 2011.

[14]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[15]  Tony Veale,et al.  Lexical Combinatorial Creativity with “ Gastronaut ” , 2006 .

[16]  Geraint A. Wiggins,et al.  A preliminary framework for description, analysis and comparison of creative systems , 2006, Knowl. Based Syst..

[17]  Hannu Toivonen,et al.  Sleep Musicalization: Automatic Music Composition from Sleep Measurements , 2012, IDA.

[18]  Tony Veale,et al.  Once More, With Feeling! Using Creative Affective Metaphors to Express Information Needs , 2013, ICCC.

[19]  Hannu Toivonen,et al.  Harnessing Constraint Programming for Poetry Composition , 2013, ICCC.

[20]  Robin C. Laney,et al.  Using Discovered, Polyphonic Patterns to Filter Computer-generated Music , 2010, ICCC.

[21]  Hugo Gonçalo Oliveira PoeTryMe : a versatile platform for poetry generation , 2012 .

[22]  Tony Veale,et al.  Less Rhyme, More Reason: Knowledge-based Poetry Generation with Feeling, Insight and Wit , 2013, ICCC.

[23]  Ethel Ong,et al.  Automatically Extracting Word Relationships as Templates for Pun Generation , 2009 .

[24]  Dan Ventura,et al.  Autonomously Managing Competing Objectives to Improve the Creation and Curation of Artifacts , 2014, ICCC.

[25]  Eduardo Miranda,et al.  Making Music with Algorithms: A Case-Study System , 1999, Computer Music Journal.

[26]  Simon Colton,et al.  Full-FACE Poetry Generation , 2012, ICCC.

[27]  Geraint A. Wiggins,et al.  SYSTEMATIC EVALUATION AND IMPROVEMENT OF STATISTICAL MODELS OF HARMONY , 2015 .

[28]  Tony Veale,et al.  Comprehending and Generating Apt Metaphors: A Web-driven, Case-based Approach to Figurative Language , 2007, AAAI.

[29]  Kush R. Varshney,et al.  A Big Data Approach to Computational Creativity , 2013, ArXiv.

[30]  Alison Pease,et al.  Proceedings of the International Conference on Computational Creativity , 2010 .

[31]  Penousal Machado,et al.  Evolving Figurative Images Using Expression-Based Evolutionary Art , 2013, ICCC.

[32]  Simon Colton,et al.  Proceedings of the Seventh International Conference on Computational Creativity , 2016 .

[33]  Dan Ventura,et al.  Autonomously Creating Quality Images , 2011, ICCC.

[34]  A. D. Ritchie The Creative Mind , 1946, Nature.

[35]  Katherine A. Brady,et al.  Computational Models of Surprise in Evaluating Creative Design , 2013 .

[36]  Peter Swire,et al.  Learning to Create Jazz Melodies Using Deep Belief Nets , 2010, ICCC.

[37]  Simon Colton,et al.  Evaluating Machine Creativity , 2001 .

[38]  Dan Ventura,et al.  Musical Motif Discovery in Non-musical Media , 2014, ICCC.

[39]  John Langford,et al.  Search-based structured prediction , 2009, Machine Learning.

[40]  Kamran Baig An act of creation , 2003, BMJ : British Medical Journal.

[41]  Kok Wai Wong,et al.  Towards a more natural and intelligent interface with embodied conversation agent , 2006 .

[42]  Tobias Kötter,et al.  Towards Creative Information Exploration Based on Koestler's Concept of Bisociation , 2012, Bisociative Knowledge Discovery.

[43]  Rob Saunders,et al.  Designing for Interest and Novelty , 2001 .

[44]  François Pachet,et al.  Non-Conformant Harmonization: the Real Book in the Style of Take 6 , 2014, ICCC.

[45]  D. Swanson Undiscovered Public Knowledge , 1986 .

[46]  Hannu Toivonen,et al.  The Officer Is Taller Than You, Who Race Yourself! Using Document Specific Word Associations in Poetry Generation , 2014, ICCC.

[47]  Dan Ventura,et al.  Establishing Appreciation in a Creative System , 2010, ICCC.

[48]  Ben Taskar,et al.  Learning structured prediction models: a large margin approach , 2005, ICML.

[49]  Dan Ventura,et al.  Automatic Composition from Non-musical Inspiration Sources , 2012, ICCC.

[50]  Bryan Duggan MATT-A System for Modelling Creativity in Traditional Irish Flute Playing , .

[51]  Jürgen Schmidhuber,et al.  Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes , 2008, ABiALS.

[52]  Kyle E. Jennings Developing Creativity: Artificial Barriers in Artificial Intelligence , 2010, Minds and Machines.

[53]  Alison Pease,et al.  Proceedings of the sixth international conference of computational creativity , 2015 .

[54]  Hannu Toivonen,et al.  Corpus-Based Generation of Content and Form in Poetry , 2012, ICCC.

[55]  John S. Gero A CURIOUS DESIGN AGENT A Computational Model of Novelty-Seeking Behaviour in Design , 2001 .

[56]  Douglas B. Lenat,et al.  EURISKO: A Program That Learns New Heuristics and Domain Concepts , 1983, Artif. Intell..

[57]  Julian Togelius,et al.  An experiment in automatic game design , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

[58]  Yanfen Hao,et al.  Learning to Understand Figurative Language: From Similes to Metaphors to Irony , 2007 .

[59]  Simon Colton,et al.  Computational Creativity: The Final Frontier? , 2012, ECAI.

[60]  Tony Veale,et al.  From Conceptual Mash-ups to Bad-ass Blends: A Robust Computational Model of Conceptual Blending , 2012, ICCC.

[61]  Elaine Chew,et al.  A Hybrid System for Automatic Generation of Style-Specific A ccompaniment , 2007 .

[62]  Jürgen Schmidhuber,et al.  Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集 高次機能の学習と創発--脳・ロボット・人間研究における新たな展開) , 2009 .

[63]  Dan Ventura,et al.  Soup Over Bean of Pure Joy: Culinary Ruminations of an Artificial Chef , 2012, ICCC.

[64]  Rob Saunders,et al.  Artificial Creative Systems and the Evolution of Language , 2011, ICCC.