GOAL: A Multi-agent Programming Language Applied to an Exploration Game

  • Koen V. Hindriks
  • Jügen Dix


Goal is a multi-agent programming language based on the BDI paradigm. It is a logic-based language that supports modular agent design based on established software engineering principles and interaction with environments using an environment interface standard (EIS). Goal recently won the multi-agent programming contest (MAPC), where two teams consisting of ten agents play against each other in order to explore and defend occupied territory on a distant planet. The MAPC game is a complex and dynamic environment that supports EIS and thus facilitates easy connection of a multi-agent system (MAS) to an environment that is remotely run. We describe the design of the multi-agent solution that won the competition, the EIS interface that was used, and the MAPC scenario.


Agent programming Environment interface Multi-agent programming contest Testing 



We would like to recognize the effort the students put into developing the HactarV2 MAS and their help in explaining their code while writing this chapter. The chapter is partly based on the MAPC paper for the HactarV2 MAS [4].


  1. 1.
    Behrens T, Dastani M, Dix J, Köster M, Novák P (2010) The multi-agent programming contest from 2005–2010. Ann Math Artif Intell 59(3):277–311CrossRefGoogle Scholar
  2. 2.
    Behrens TM, Hindriks KV, Dix J (2011) Towards an environment interface standard for agent platforms. Ann Math Artif Intell 61(4):261–295CrossRefzbMATHGoogle Scholar
  3. 3.
    Bordini R, Braubach L, Dastani M, Seghrouchni AEF, Gomez-Sanz J, Leite J, O’Hare G, Pokahr A, Ricci A (2006) A survey of programming languages and platforms for multi-agent systems. Informatica 30(1):33–44zbMATHGoogle Scholar
  4. 4.
    Dekker M, Hameete P, Hegemans M, Leysen S, van den Oever J, Smits J, Hindriks KV (2012) Hactarv2: an agent team strategy based on implicit coordination. In: Dennis L, Boissier O, Bordini RH (eds) 9th International Workshop, ProMAS 2011, Taipei, Taiwan, 3 May 2011, Revised Selected Papers. LNAI, vol 7217, pp 173–184Google Scholar
  5. 5.
    Eaton J, Redmayne J, Thordsen M (2007) Joint analysis handbook, 3rd edn. Joint Analysis and Lessons Learned Centre, Lisbon. Google Scholar
  6. 6.
    Freeman E, Freeman E, Sierra K, Bates B (2004) Head first design patterns, 1st edn. O’Reilly Media, Inc., SebastopolGoogle Scholar
  7. 7.
    Georgeff MP, Pell B, Pollack ME, Tambe M, Wooldridge M (1999) The belief-desire-intention model of agency. In: Proceedings of the 5th international workshop on intelligent agents, vol V. Agent theories, architectures, and languages (ATAL ’98). Springer, Berlin, pp 1–10Google Scholar
  8. 8.
    Hindriks K (2009) Programming rational agents in goal. In: Multi-agent programming: languages, tools and applications. Springer, Heidelberg, pp 119–157Google Scholar
  9. 9.
    Hindriks K, de Boer FS, van der Hoek W, Meyer J (2001) Agent programming with declarative goals. In: Intelligent agents VII agent theories architectures and languages. Springer, Berlin, pp 248–257Google Scholar
  10. 10.
    Hindriks K, van Riemsdijk B, Behrens T, Korstanje R, Kraayenbrink N, Pasman W, de Rijk L (2011) Unreal Goal bots. In: Dignum F (ed) Agents for games and simulations, vol II. Lecture notes in computer science, vol 6525. Springer, Berlin, pp 1–18.
  11. 11.
    Newell A (1981) The knowledge level. AI Mag 2(2):1–20MathSciNetGoogle Scholar
  12. 12.
    Nguyen C, Perini A, Bernon C, Pavn J, Thangarajah J (2011) Testing in multi-agent systems. In: Gleizes MP, Gomez-Sanz J (eds) Agent-oriented software engineering, vol X. Lecture notes in computer science, vol 6038. Springer, Berlin, pp 180–190Google Scholar
  13. 13.
    Padgham L, Winikoff M (2003) Prometheus: a methodology for developing intelligent agents. In: Proceedings of the 3rd international conference on agent-oriented software engineering, vol III (AOSE’02). Springer, Berlin, pp 174–185Google Scholar
  14. 14.
    Schwaber K (1995) Scrum development process. In: Proceedings of the 10th annual ACM conference on object oriented programming systems, languages, and applications (OOPSLA), pp 117–134Google Scholar
  15. 15.
    Shapiro L, Sterling E (1994) The art of prolog: advanced programming techniques. MIT Press, CambridgezbMATHGoogle Scholar
  16. 16.
    The Goal website (2012).
  17. 17.
    The iceScrum website (2012).
  18. 18.
    The Multi-Agent Programming Contest website (2012).
  19. 19.
    The Multi-Agent Programming Contest 2011 website (2012).
  20. 20.
    van Riemsdijk MB, Hindriks KV, Jonker CM (2012) An empirical study of cognitive agent programs. Multiagent Grid Syst 8(2):187–222Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.Clausthal University of TechnologyClausthal-ZellerfeldGermany

Personalised recommendations