--- abstract: |- Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human player. This requires a way to create accurate models of players' driving styles, as well as a tentative definition of when a racing track is fun, both of which are provided. We believe this approach opens up interesting new research questions and is potentially applicable to commercial racing games. altloc: - http://privatewww.essex.ac.uk/~rdenar/Togelius2007Towards.pdf chapter: ~ commentary: ~ commref: ~ confdates: 1st-5th April 2007 conference: 'IEEE Symposium on Computational Intelligence and Games, 2007. (CIG2007)' confloc: Hawaii contact_email: ~ creators_id: [] creators_name: - family: Togelius given: Julian honourific: '' lineage: '' - family: De Nardi given: Renzo honourific: '' lineage: '' - family: Lucas given: Simon M. honourific: '' lineage: '' date: 2007 date_type: published datestamp: 2007-05-28 department: ~ dir: disk0/00/00/55/73 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 5573 fileinfo: /style/images/fileicons/application_pdf.png;/5573/1/Togelius2007Towards.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'Games, Car Racing, Evolution, Neural Networks' lastmod: 2011-03-11 08:56:51 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: IEEE refereed: TRUE referencetext: |- G. Kendall and S. M. Lucas, Proceedings of the IEEE Symposium on Computational Intelligence and Games. IEEE Press, 2005. P. Spronck, Adaptive game ai, Ph.D. dissertation, University of Maastricht, 2005. I. Tanev, M. Joachimczak, H. Hemmi, and K. Shimohara, Evolution of the driving styles of anticipatory agent remotely operating a scaled model of racing car,in Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC-2005), 2005, pp. 1891-1898. B. Chaperot and C. Fyfe, Improving artificial intelligence in a Motocross game, in IEEE Symposium on Computational Intelligence and Games, 2006. J. Togelius and S. M. Lucas, Evolving controllers for simulated car racing, in Proceedings of the Congress on Evolutionary Computation, 2005. J. Togelius and S. M. Lucas,Evolving robust and specialized car racing skills,in Proceedings of the IEEE Congress on Evolutionary Computation, 2006. K. Wloch and P. J. Bentley, Optimising the performance of a formula one car using a genetic algorithm, in Proceedings of Eighth International Conference on Parallel Problem Solving From Nature, 2004, pp. 702-711. D. Cliff, Computational neuroethology: a provisional manifesto, in Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats, 1991, pp. 29-39. D. Floreano, T. Kato, D. Marocco, and E. Sauser,Coevolution of Active vision and feature selection, Biological Cybernetics, vol. 90,pp. 218-228, 2004. J. Togelius and S. M. Lucas, Arms races and car races,in Proceedings of Parallel Problem Solving from Nature. Springer, 2006. D. A. Pomerleau, Neural network vision for robot driving in The Handbook of Brain Theory and Neural Networks, 1995. J. Togelius, R. De Nardi, and S. M. Lucas, Making racing fun through player modeling and track evolution, in Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games, 2006. D.A. Jirenhed, G. Hesslow, and T. Ziemke, Exploring internal simulation of perception in mobile robots,in Proceedings of the Fourth European Workshop on Advanced Mobile Robots, 2001, pp. 107-113. G. N. Yannakakis and M. Maragoudakis, Player modeling impact on players entertainment in computer games, in User Modeling, 2005, pp. 74-78. D. Ashlock, T. Manikas, and K. Ashenayi, Evolving a diverse collection of robot path planning problems, in Proceedings of the Congress On Evolutionary Computation, 2006, pp. 6728- 6735. T. W. Malone, What makes things fun to learn? heuristics for designing instructional computer games, in Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems, 1980, pp. 162-169. R. Koster, A theory of fun for game design. Paraglyph press, 2004. D. Gamez, R. Newcombe, O. Holland, and R. Knight, Two simulation tools for biologically inspired virtual robotics, in Proceedings of the IEEE 5th Chapter Conference on Advances in Cybernetic Systems, 2006, pp. 85-90. D. S. Ebert, F. K. Musgrave, D. Peachey, K. Perlin, and S. Worley, Texturing and Modeling: A Procedural Approach. Morgan Kaufmann, 2002. S. Greuter, J. Parker, N. Stewart, and G. Leach, Real-time procedural generation of `pseudo infinite' cities, in Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia, 2003. G. N. Yannakakis and J. Hallam, Towards capturing and enhancing entertainment in computer games, in Proceedings of the Hellenic Conference on Artificial Intelligence, 2006, pp. 432-442. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:10:46 subjects: - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: ' Towards automatic personalised content creation for racing games' type: confpaper userid: 7072 volume: ~