Sentient Sketchbook: Computer-aided game level authoring

This paper introduces the Sentient Sketchbook, a tool which supports a designer in the creation of game levels. Using map sketches to alleviate designer effort, the tool automates playability checks and evaluations and visualizes significant gameplay properties. This paper also introduces constrained novelty search via a two-population paradigm for generating, in real-time, alternatives to the author’s design and evaluates its potential against current approaches. The paper concludes with a small-scale user study in which industry experts interact with the Sentient Sketchbook to design game levels. Results demonstrate the tool’s potential and provide directions for its improvement.

[1]  Todd Lubart,et al.  How can computers be partners in the creative process: Classification and commentary on the Special Issue , 2005, Int. J. Hum. Comput. Stud..

[2]  Kenneth O. Stanley,et al.  Revising the evolutionary computation abstraction: minimal criteria novelty search , 2010, GECCO '10.

[3]  Julian Togelius,et al.  Generating Map Sketches for Strategy Games , 2013, EvoApplications.

[4]  Georgios N. Yannakakis.  Game AI revisited , 2012, CF '12.

[5]  Michael Mateas,et al.  Tanagra: Reactive Planning and Constraint Solving for Mixed-Initiative Level Design , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[6]  Philippe Pasquier,et al.  A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[7]  Julian Togelius,et al.  Search-Based Procedural Content Generation: A Taxonomy and Survey , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[8]  Kenneth O. Stanley,et al.  Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space , 2011, Evolutionary Computation.

[9]  Kenneth O. Stanley,et al.  Evolving content in the Galactic Arms Race video game , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[10]  J. Francis Reintjes,et al.  Numerical control: making a new technology , 1991 .

[11]  Steven Orla Kimbrough,et al.  On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch , 2008, Eur. J. Oper. Res..

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

[13]  Frédéric Maire,et al.  Evolutionary Game Design , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[14]  Zbigniew Michalewicz,et al.  Evolutionary Computation at the Edge of Feasibility , 1996, PPSN.

[15]  Z. Michalewicz.  Do Not Kill Unfeasible Individuals , 1995 .

[16]  Julian Togelius,et al.  Limitations of Choice-Based Interactive Evolution for Game Level Design , 2012, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

[17]  Julian Togelius,et al.  Neuroevolutionary constrained optimization for content creation , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[18]  Julian Togelius,et al.  Experience-Driven Procedural Content Generation , 2011, IEEE Transactions on Affective Computing.

[19]  Daniel A. Ashlock,et al.  Search-Based Procedural Generation of Maze-Like Levels , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[20]  Kenneth O. Stanley,et al.  Abandoning Objectives: Evolution Through the Search for Novelty Alone , 2011, Evolutionary Computation.

[21]  Julian Togelius,et al.  Adapting Models of Visual Aesthetics for Personalized Content Creation , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[22]  Julian Togelius,et al.  Towards automatic personalised content creation for racing games , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[23]  Julian Togelius,et al.  Enhancements to constrained novelty search: two-population novelty search for generating game content , 2013, GECCO '13.

[24]  Michael Mateas,et al.  Answer Set Programming for Procedural Content Generation: A Design Space Approach , 2011, IEEE Transactions on Computational Intelligence and AI in Games.