Applied Intelligence

, Volume 48, Issue 5, pp 1161–1175 | Cite as

SpringBoard: game-agnostic tool for scenario editing with meta-programming support

  • Gajo Petrovic
  • Hamido Fujita


Although we have recently seen an increase of good, free game engine editors, general purpose scenario (level) editors are still lagging behind in terms of functionalities and ease of use. Using them to create game scenarios can be difficult as they often expose general engine capabilities instead of limiting the toolset to fit game-specific requirements. They often require programming skills to use, which introduce additional user skill requirements, and configuring them for a specific game can be equally difficult. In this paper we have developed SpringBoard, an open source scenario editor for games using the SpringRTS engine. Extending it to support game and level requirements is achieved with multi-level meta-programming, while still providing a system that is integrated with the GUI editor and therefore intuitive to use. Our meta-programming system has support for trigger elements (events, functions and actions), custom (composite) data types, scoped data access, higher order functions and actions, and data synchronization mechanics. This novel approach allows us to have the full expressiveness of the underlying programming language, while exposing a user-friendly GUI that consists of terminology familiar to the domain expert.


Scenario editor Level editor Game creation tool Meta-programming 


  1. 1.
    Moanes A, Ibrahim AH, Hosny A (2016) xgame: a novel approach for developing accessible mobile games. In: New trends in software methodologies, tools and techniques, vol 286 of frontiers in artificial intelligence and applications. IOS Press, pp 315–321Google Scholar
  2. 2.
    Anderson EF, McLoughlin L, Watson J, Holmes S, Jones P, Pallett H, Smith B (2013) Choosing the infrastructure for entertainment and serious computer games - a whiteroom benchmark for game engine selection. In: 2013 5th international conference on games and virtual worlds for serious applications (VS-GAMES). IEEE, pp 1–8Google Scholar
  3. 3.
    Brassai B, Varga B, Simon K, Torok-Vistai T (2014) Geoquesting: mobile adventure game and web-based game editor. In: 2014 IEEE 12th international symposium on intelligent systems and informatics (SISY). IEEE, pp 99–103Google Scholar
  4. 4.
    Dobrovsky A, Borghoff UM, Hofmann M (2016) An approach to interactive deep reinforcement learning for serious games. In: 2016 7th IEEE international conference on cognitive infocommunications (CogInfoCom). IEEE, pp 000085–000090Google Scholar
  5. 5.
    Dondlinger MJ (2007) Educational video game design: a review of the literature. Journal of Applied Educational Technology 4(1):21–31Google Scholar
  6. 6.
    Guo X, Singh S, Lee H, Lewis RL, Wang X (2014) Deep learning for real-time atari game play using offline Monte-Carlo tree search planning. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27. Curran Associates, Inc., pp 3338–3346Google Scholar
  7. 7.
    Gustavsson PM, Lubera M, Lind H, Blomberg J (2009) J wemmergård Lessons learned from the implementation of a battle management language in a general scenario editor. In: Fall simulation interoperability workshopGoogle Scholar
  8. 8.
    Hausknecht MJ, Stone P (2015) Deep recurrent q-learning for partially observable mdps. In: AAAI fall symposium seriesGoogle Scholar
  9. 9.
    Lv Z, Tek A, Da Silva F, Empereur-mot C, Chavent M, Baaden M (2013) Game on, science - how video game technology may help biologists tackle visualization challenges. PLoS ONE 8(3): e57990CrossRefGoogle Scholar
  10. 10.
    Mattheiss E, Regal G, Sellitsch D, Tscheligi M (2017) User-centred design with visually impaired pupils: a case study of a game editor for orientation and mobility training. Int J Child-Comput Interact 11:12–18CrossRefGoogle Scholar
  11. 11.
    Mnih V, Badia AP, Mirza M, Graves A, Lillicrap T, Harley T, Silver D, Kavukcuoglu K (2016) Asynchronous methods for deep reinforcement learning. In: International conference on machine learning, pp 1928–1937Google Scholar
  12. 12.
    Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M A (2013) Playing atari with deep reinforcement learning. In: NIPS deep learning workshopGoogle Scholar
  13. 13.
    Oh J, Guo X, Lee H, Lewis RL, Singh S (2015) Action-conditional video prediction using deep networks in atari games. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28. Curran Associates, Inc., pp 2863–2871Google Scholar
  14. 14.
    Oswald P, Tost J, Wettach R (2014) The real augmented reality: real-time game editor in a spatial augmented environment. In: Proceedings of the 11th conference on advances in computer entertainment technology - ACE 14. ACM Press, pp 1–4Google Scholar
  15. 15.
    Park W-S, Hong H-K, WhangBo T-K (2010) Design and implementation of game scenario editor. Journal of Korea Game Society 10(1):115–125Google Scholar
  16. 16.
    Thompson D, Baranowski T, Buday R, Baranowski J, Thompson V, Jago R, Griffith MJ (2010) Serious video games for health: how behavioral science guided the development of a serious video game. Simul Games 41(4):587–606CrossRefGoogle Scholar
  17. 17.
    Ullner F, Lundgren A (2008) Lessons learned from implementing a msdl scenario editor. Student PaperGoogle Scholar
  18. 18.
    van der Spek ED, van Oostendorp H, Meyer J-JC (2013) Introducing surprising events can stimulate deep learning in a serious game. Br J Educ Technol 44(1):156–169CrossRefGoogle Scholar
  19. 19.
    Wyeld T, Barbuto Z (2014) Don’t hide the code!: empowering novice and beginner programmers using a HTML game editor. In: 2014 18th international conference on information visualisation. IEEE, pp 125–131Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Iwate Prefectural UniversityTakizawaJapan

Personalised recommendations