Modeling Children's Entertainment in the Playware Playground

This paper introduces quantitative measurements/metrics of qualitative entertainment features within interactive playgrounds inspired by computer games and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. For this purpose the innovative Playware playground is presented and a quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced. Evolving artificial neural networks (ANNs) are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment according to player reaction time with the game. The limitations of the methodology and the extensibility of the proposed approach to other genres of digital entertainment are discussed

[1]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[2]  Georgios N. Yannakakis,et al.  A scheme for creating digital entertainment with substance , 2005 .

[3]  Aude Billard,et al.  Evolving Opponents for Interesting Interactive Computer Games , 2004 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[6]  Alex J. Champandard.  AI Game Development , 2003 .

[7]  Thore Graepel,et al.  LEARNING TO FIGHT , 2004 .

[8]  David L. Westbrook,et al.  Intelligent modeling of the user in interactive entertainment , 2002 .

[9]  Georgios N. Yannakakis,et al.  A Generic Approach for Obtaining Higher Entertainment in Predator/Prey Computer Games , 2005 .

[10]  Peter W. McOwan,et al.  An adaptive methodology for synthesising mobile phone games using genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[11]  Martin Lindström,et al.  BrandChild: Remarkable Insights into the Minds of Today's Global Kids and Their Relationship with Brands , 2003 .

[12]  James W. Davis,et al.  The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment , 1999, Presence.

[13]  Janis Peacock Bellack,et al.  The disappearance of childhood , 1986 .

[14]  S. Kline.  Out of the Garden: Toys, Tv, and Children's Culture in the Age of Marketing , 1993 .

[15]  N. Taatgen,et al.  How to construct a believable opponent using cognitive modeling in the game of set , 2003 .

[16]  Jerry Herron,et al.  Out of the Garden: Toys and Children's Culture in the Age of TV Marketing , 1995 .

[17]  Joseph A. Paradiso,et al.  Z-Tiles: building blocks for modular, pressure-sensing floorspaces , 2004, CHI EA '04.

[18]  Alexander Nareyek,et al.  Intelligent Agents for Computer Games , 2006 .

[19]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[20]  Hiroyuki Iida,et al.  A metric for entertainment of boardgames: its implication for evolution of chess variants , 2002, IWEC.

[21]  Georgios N. Yannakakis,et al.  Evolving opponents for interesting interactive computer games , 2004 .

[22]  Gregory D. Abowd,et al.  The smart floor: a mechanism for natural user identification and tracking , 2000, CHI Extended Abstracts.

[23]  Vincent Corruble,et al.  Extending Reinforcement Learning to Provide Dynamic Game Balancing , 2005 .

[24]  Henrik Hautop Lund,et al.  Playware technology for physically activating play , 2005, Artificial Life and Robotics.

[25]  Georgios N. Yannakakis.  AI in computer games : generating interesting interactive opponents by the use of evolutionary computation , 2005 .

[26]  John E. Laird,et al.  Human-Level AI's Killer Application: Interactive Computer Games , 2000, AI Mag..

[27]  Naty Hoffman,et al.  Game development , 2006, SIGGRAPH Courses.