Encouraging p-creative behaviour with computational curiosity

A concept, design or other artefact is p-creative when it is simultaneously novel and valuable for a specific individual. This is defined by contrast to h-creative artefacts, which are novel and valuable for a society as a whole. When we talk about p-creativity in computational systems we usually mean that something is creative to the system itself: the system has its own experiences and goals, and with them judges novelty and value. We propose an alternative approach aimed at simulating what a specific human user will find p-creative in order to stimulate that user towards pcreative behaviour. We define a framework for simulating curiosity, explore several domains in which it could be applied, and describe some preliminary results from a system designed to suggest papers for students to read that they would find surprising. We end the paper with a discussion of how this model can be extended to generate framing narratives that combine content from different artefacts that encourages p-creative behaviour.

[1]  Andrew Dillon,et al.  Design rationale: Concepts, techniques, and use , 1997 .

[2]  Kazjon Grace,et al.  What to expect when you're expecting: The role of unexpectedness in computationally evaluating creativity , 2014, ICCC.

[3]  Rob Saunders,et al.  Curious Design Agents and Artificial Creativity - A Synthetic Approach to the Study of Creative Behaviour , 2001 .

[4]  Nuttapong Chentanez,et al.  Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .

[5]  Judy Kay,et al.  The um toolkit for cooperative user modelling , 2005, User Modeling and User-Adapted Interaction.

[6]  Simon Colton,et al.  Modelling serendipity in a computational context , 2014, ArXiv.

[7]  D. Berlyne.  Curiosity and exploration. , 1966, Science.

[8]  Thomas G. Reio,et al.  Field Investigation of the Relationship among Adult Curiosity, Workplace Learning, and Job Performance. [and] Invited Reaction: Relationship among Adult Curiosity, Workplace Learning, and Job Performance. , 2000 .

[9]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[10]  Emanuele Castano,et al.  Reading Literary Fiction Improves Theory of Mind , 2013, Science.

[11]  M. Csíkszentmihályi,et al.  The measurement of flow in everyday life: toward a theory of emergent motivation. , 1992, Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.

[12]  David G. Ullman,et al.  Design rationale: Concepts, techniques, and use , 1997 .

[13]  Warren Berger.  CAD Monkeys, Dinosaur Babies, and T-Shaped People: Inside the World of Design Thinking and How It Can Spark Creativity and Innovation , 2010 .

[14]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[15]  Wanda Pratt,et al.  How to evaluate technologies for health behavior change in HCI research , 2011, CHI.

[16]  M. Boden.  The creative mind : myths & mechanisms , 1991 .

[17]  Pierre Baldi,et al.  Of bits and wows: A Bayesian theory of surprise with applications to attention , 2010, Neural Networks.

[18]  Tod Mijanovich,et al.  Dietary Variety Is Inversely Associated with Body Adiposity among US Adults Using a Novel Food Diversity Index 1–3 , 2022 .

[19]  Mark A. Runco,et al.  Eminent creativity, everyday creativity, and health , 1998 .

[20]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[21]  John D. Lafferty,et al.  A correlated topic model of Science , 2007, 0708.3601.

[22]  Kathryn E. Merrick,et al.  Motivated Reinforcement Learning - Curious Characters for Multiuser Games , 2009 .

[23]  B. J. Fogg,et al.  A behavior model for persuasive design , 2009, Persuasive '09.

[24]  Jeffrey Heer,et al.  Termite: visualization techniques for assessing textual topic models , 2012, AVI.

[25]  David C. Wilson,et al.  Personalised Specific Curiosity for Computational Design Systems , 2017 .

[26]  Judith Masthoff,et al.  Designing and Evaluating Explanations for Recommender Systems , 2011, Recommender Systems Handbook.

[27]  Simon Colton,et al.  On the Notion of Framing in Computational Creativity , 2012, ICCC.

[28]  Rafael Pérez y Pérez,et al.  A Computer Model for the Generation of Visual Compositions , 2013, ICCC.

[29]  Jürgen Schmidhuber,et al.  Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.

[30]  Katherine A. Brady,et al.  Modeling Expectation for Evaluating Surprise in Design Creativity , 2015 .

[31]  Rafael Pérez y Pérez,et al.  MEXICA: A computer model of a cognitive account of creative writing , 2001, J. Exp. Theor. Artif. Intell..

[32]  Sharon Bailin.  CREATIVITY IN CONTEXT , 2002 .

[33]  Masaki Suwa,et al.  Unexpected discoveries and S-invention of design requirements , 2000 .

[34]  Rafael Pérez y Pérez,et al.  The ERI-Designer: A Computer Model for the Arrangement of Furniture , 2010, Minds and Machines.

[35]  John S. Gero,et al.  Design and other types of fixation , 1996 .

[36]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.

[37]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[38]  Margaret E. Roberts,et al.  stm: An R Package for Structural Topic Models , 2019, Journal of Statistical Software.