Computational Creativity Research: Towards Creative Machines

Computational Creativity, Concept Invention, and General Intelligence in their own right all are flourishing research disciplines producing surprising and captivating results that continuously influence and change our view on where the limits of intelligent machines lie, each day pushing the boundaries a bit further. By 2014, all three fields also have left their marks on everyday life machine-composed music has been performed in concert halls, automated theorem provers are accepted tools in enterprises R&D departments, and cognitive architectures are being integrated in pilot assistance systems for next generation airplanes. Still, although the corresponding aims and goals are clearly similar (as are the common methods and approaches), the developments in each of these areas have happened mostly individually within the respective community and without closer relationships to the goings-on in the other two disciplines. In order to overcome this gap and to provide a common platform for interaction and exchange between the different directions, the International Workshops on Computational Creativity, Concept Invention, and General Intelligence (C3GI) have been started. At ECAI-2012 and IJCAI-2013, the first and second edition of C3GI each gathered researchers from all three fields, presenting recent developments and results from their research and in dialogue and joint debates bridging the disciplinary boundaries. The chapters contained in this book are based on expanded versions of accepted contributions to the workshops and additional selected contributions by renowned researchers in the relevant fields. Individually, they give an account of the state-of-the-art in their respective area, discussing both, theoretical approaches as well as implemented systems. When taken together and looked at from an integrative perspective, the book in its totality offers a starting point for a (re)integration of Computational Creativity, Concept Invention, and General Intelligence, making visible common lines of work and theoretical underpinnings, and pointing at chances and opportunities arising from the interplay of the three fields.

[1]  Geraint A. Wiggins,et al.  The Role of Expectation and Probabilistic Learning in Auditory Boundary Perception: A Model Comparison , 2010, Perception.

[2]  Ian H. Witten,et al.  Multiple viewpoint systems for music prediction , 1995 .

[3]  J. Saffran,et al.  Absolute pitch in infant auditory learning: evidence for developmental reorganization. , 2001, Developmental psychology.

[4]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[5]  Arash Eshghi,et al.  Probabilistic induction for an incremental semantic grammar , 2013, IWCS.

[6]  J. Fodor.  Special sciences (or: The disunity of science as a working hypothesis) , 1974, Synthese.

[7]  Ian H. Witten,et al.  PREDICTION AND ENTROPY OF MUSIC , 1990 .

[8]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[9]  Heiner Stuckenschmidt,et al.  A Pattern-based Ontology Matching Approach for Detecting Complex Correspondences , 2009, OM.

[10]  Geraint A. Wiggins.  Defining Inspiration? Modelling non-conscious creative process , 2011 .

[11]  Paul R. Cohen,et al.  An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes , 2001, IDA.

[12]  Geraint A. Wiggins.  ‘‘I let the music speak’’: Cross-domain application of a cognitive model of musical learning , 2011 .

[13]  Geraint A. Wiggins,et al.  Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints in the Harmonization Problem , 2013 .

[14]  Ron McClamrock,et al.  Marr's three levels: A re-evaluation , 1991, Minds and Machines.

[15]  Geraint A. Wiggins,et al.  Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music , 2013, Cognitive, Affective, & Behavioral Neuroscience.

[16]  Geraint A. Wiggins,et al.  Auditory Expectation: The Information Dynamics of Music Perception and Cognition , 2012, Top. Cogn. Sci..

[17]  D. Corkill.  Blackboard Systems , 1991 .

[18]  John R. Anderson,et al.  Learning Artificial Grammars With Competitive Chunking , 1990 .

[19]  J. Dupré.  The Conscious Mind: In Search of a Fundamental Theory , 2000 .

[20]  A. Meltzoff,et al.  The development of gaze following and its relation to language. , 2005, Developmental science.

[21]  Gordon Plotkin,et al.  A Note on Inductive Generalization , 2008 .

[22]  M. V. Mulken,et al.  The impact of perceived complexity, deviation and comprehension on the appreciation of visual metaphor in advertising across three European countries , 2010 .

[23]  Geraint A. Wiggins.  Computer models of (music) cognition , 2011 .

[24]  James F. Glazebrook.  Embodiment and the inner life: Cognition and consciousness in the space of possible minds, M. Shanahan. Oxford University Press (2010) , 2014, Cogn. Syst. Res..

[25]  Ashok K. Goel,et al.  Behavior Patterns: Bridging Conceptual Models and Agent-Based Simulations in Interactive Learning Environments , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[26]  Angela Schwering,et al.  A Computational Model for Visual Metaphors. Interpreting Creative Visual Advertisements , 2009, ICAART.

[27]  Adwait Ratnaparkhi,et al.  A Maximum Entropy Approach to Identifying Sentence Boundaries , 1997, ANLP.

[28]  Noam Chomsky,et al.  The evolution of the language faculty: Clarifications and implications , 2005, Cognition.

[29]  Brian Walshe,et al.  Identifying Complex Semantic Matches , 2012, ESWC.

[30]  Cristóbal Pagán Cánovas.  Erotic Emissions in Greek Poetry: A Generic Integration Network , 2010 .

[31]  Francisco C. Pereira.  Optimality Principles for Conceptual Blending: A First Computational Approach , 2003 .

[32]  E. Holmes.  The Life of Mozart: Including his Correspondence , 1991 .

[33]  Kai-Uwe Kühnberger,et al.  COINVENT: Towards a Computational Concept Invention Theory , 2014, ICCC.

[34]  B. Baars.  A cognitive theory of consciousness , 1988 .

[35]  John Hale,et al.  A Probabilistic Earley Parser as a Psycholinguistic Model , 2001, NAACL.

[36]  Leonard B. Meyer.  Emotion and Meaning in Music , 1957 .

[37]  Angela Schwering,et al.  Syntactic principles of heuristic-driven theory projection , 2009, Cognitive Systems Research.

[38]  Ashok K. Goel,et al.  Combining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents , 2008, AIIDE.

[39]  Geraint A. Wiggins,et al.  Evaluating Cognitive Models of Musical Composition , 2007 .

[40]  T. Veale,et al.  Computation and Blending , 2001 .

[41]  Michael Luck,et al.  Agent technology: Enabling next generation computing , 2003 .

[42]  Mark Turner,et al.  The Way We Imagine1 , 2007 .

[43]  C. Neuper,et al.  The creative brain: Investigation of brain activity during creative problem solving by means of EEG and FMRI , 2009, Human brain mapping.

[44]  Daniel Jurafsky,et al.  A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing , 2001, NIPS.

[45]  Henkjan Honing,et al.  Computational modeling of music cognition: a case study on model selection. , 2006 .

[46]  Marcela Vegetti,et al.  Towards ontology evaluation across the life cycleThe Ontology Summit 2013 , 2013, Appl. Ontology.

[47]  A. Vinter,et al.  PARSER: A Model for Word Segmentation , 1998 .

[48]  C.G. Pereira,et al.  Creativity and Artificial Intelligence: A Conceptual Blending Approach , 2007 .

[49]  Alan Marsden.  Response to Geraint Wiggins , 2012 .

[50]  Alex McLean,et al.  Unifying Conceptual Spaces: Concept Formation in Musical Creative Systems , 2010, Minds and Machines.

[51]  Patrick Henry Winston,et al.  Artificial intelligence (3rd ed.) , 1992 .

[52]  G. Fairbanks,et al.  A psychophysical investigation of vowel formants. , 1961, Journal of speech and hearing research.

[53]  J. Pine,et al.  Chunking mechanisms in human learning , 2001, Trends in Cognitive Sciences.

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

[55]  Geraint A. Wiggins,et al.  Methods for Combining Statistical Models of Music , 2004, CMMR.

[56]  M. Pearce,et al.  Sweet Anticipation : Music and the Psychology of Expectation , 2007 .

[57]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[58]  R. Núñez.  Creating mathematical infinities: Metaphor, blending, and the beauty of transfinite cardinals , 2005 .

[59]  Marcus T. Pearce,et al.  The construction and evaluation of statistical models of melodic structure in music perception and composition , 2005 .

[60]  Immanuel Normann.  Automated Theory Interpretation , 2009 .

[61]  Geraint A. Wiggins,et al.  EXPECTATION IN MELODY: THE INFLUENCE OF CONTEXT AND LEARNING , 2006 .

[62]  Geraint A. Wiggins.  Searching for computational creativity , 2006, New Generation Computing.

[63]  William D. Marslen-Wilson,et al.  Function and process in spoken word recognition: A tutorial review , 1984 .

[64]  Chris Mellish,et al.  Statistical Learning of Harmonic Movement , 1999 .

[65]  B. Merker.  The efference cascade, consciousness, and its self: naturalizing the first person pivot of action control , 2013, Front. Psychol..

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

[67]  G. Wallas.  The art of thought , 1926 .

[68]  María Herrojo Ruiz,et al.  Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation , 2010, NeuroImage.

[69]  Mark Turner,et al.  The Origin of Ideas: Blending, Creativity, and the Human Spark , 2014 .

[70]  Peter Gärdenfors,et al.  Conceptual spaces - the geometry of thought , 2000 .