Skip to main content
SpringerLink
Log in

General Language Evolution in General Game Playing

  • Conference paper
  • First Online:
AI 2018: Advances in Artificial Intelligence (AI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11320))

Included in the following conference series:

Abstract

General Game Playing (GGP) is concerned with the development of programs capable of expertly playing a game by just receiving its rules and without human intervention. Its standard Game Description Language (GDL) has been extended so as to include incomplete information games. The extended version is named as GDL-II. Different algorithms were recommended to play games in GDL-II, however, none of them can solve coordination games properly. One reason for this shortcoming is their inability to generate the necessary coordination language. On the other side, most existing language evolution techniques focus on generating a common language without considering its generality or its use for problem solving. In this paper, we will extend GGP with language evolution to develop a general language generation technique. The new technique can be combined with GGP algorithms for incomplete-information games and assist players in automatically generating a common language to solve cooperation problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Later in this paper, we use a shorter version to represent a mustRule in figures. It is structured as <action abbreviation>-<perception abbreviation>. For example, will be shown as A-Red.

  2. 2.

    An agent is referred to as a primitive version of another agent if the language of the former is a subset of the language of the latter.

References

  1. Bleys, J., Loetzsch, M., Spranger, M., Steels, L.: The grounded colour naming game. Institute of Electrical and Electronics Engineers (2009)

    Google Scholar 

  2. Boutilier, C.: Sequential optimality and coordination in multiagent systems. In: IJCAI, vol. 99, pp. 478–485 (1999)

    Google Scholar 

  3. Černỳ, J.: Playing general imperfect-information games using game-theoretic algorithms. Bachelor’s thesis, Czech Technical University in Prague, Czech Republic (2014)

    Google Scholar 

  4. Chitizadeh, A., Thielscher, M.: Iterative tree search in general game playing with incomplete information. In: Computer Games Workshop at IJCAI 2018. https://www.cse.unsw.edu.au/~mit/Papers/CGW18.pdf

  5. Fenster, M., Kraus, S., Rosenschein, J.S.: Coordination without communication: experimental validation of focal point techniques. In: International Conference on Manufacturing Systems, pp. 102–108 (1995)

    Google Scholar 

  6. Foerster, J., Assael, I.A., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 2137–2145 (2016)

    Google Scholar 

  7. Geißer, F., Keller, T., Mattmüller, R.: Past, present, and future: an optimal online algorithm for single-player GDL-II games. In: ECAI, vol. 14, pp. 357–362 (2014)

    Google Scholar 

  8. Genesereth, M., Thielscher, M.: General Game Playing. Morgan & Claypool Publishers, San Rafael (2014)

    MATH  Google Scholar 

  9. Gonzalez, M., Watson, R., Bullock, S.: Minimally sufficient conditions for the evolution of social learning and the emergence of non-genetic evolutionary systems. Artif. Life 23(4), 493–517 (2017)

    Article  Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  11. LaVictoire, P., Fallenstein, B., Yudkowsky, E., Barasz, M., Christiano, P., Herreshoff, M.: Program equilibrium in the prisoner’s dilemma via Löb’s theorem. In: AAAI Workshop on Multiagent Interaction without Prior Coordination (2014)

    Google Scholar 

  12. Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General game playing: game description language specification (2008). http://logic.stanford.edu/reports/LG-2006-01.pdf

  13. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O.P., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Advances in Neural Information Processing Systems, pp. 6382–6393 (2017)

    Google Scholar 

  14. Owen, G.: Game Theory. Emerald Group Publishing Limited, Bingley (2013)

    MATH  Google Scholar 

  15. Schofield, M.J., Thielscher, M.: Lifting model sampling for general game playing to incomplete-information models. In: AAAI, vol. 15, pp. 3585–3591 (2015)

    Google Scholar 

  16. Skubic, M., et al.: Spatial language for human-robot dialogs. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 34(2), 154–167 (2004)

    Article  Google Scholar 

  17. Steels, L.: A self-organizing spatial vocabulary. Artif. Life 2(3), 319–332 (1995)

    Article  Google Scholar 

  18. Steels, L.: Language games for autonomous robots. IEEE Intell. Syst. 16(5), 16–22 (2001)

    Google Scholar 

  19. Steels, L., Kaplan, F.: Aibo’s first words: the social learning of language and meaning. Evol. Commun. 4(1), 3–32 (2000)

    Google Scholar 

  20. Steels, L., Kaplan, F., McIntyre, A., Van Looveren, J.: Crucial factors in the origins of word-meaning. Trans. Lang. 2(1), 2–4 (2002)

    Google Scholar 

  21. Steels, L., Spranger, M.: Can body language shape body image? In: Artificial Life, pp. 577–584 (2008)

    Google Scholar 

  22. Tennenholtz, M.: Program equilibrium. Games Econ. Behav. 49(2), 363–373 (2004)

    Article  MathSciNet  Google Scholar 

  23. Thielscher, M.: A general game description language for incomplete information games. In: AAAI, vol. 10, pp. 994–999 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UNSW Sydney, Sydney, NSW, 2052, Australia

    Armin Chitizadeh & Michael Thielscher

Authors
  1. Armin Chitizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Thielscher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Armin Chitizadeh .

Editor information

Editors and Affiliations

  1. University of Canterbury, Christchurch, New Zealand

    Tanja Mitrovic

  2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

  3. RMIT University, Melbourne, VIC, Australia

    Xiaodong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chitizadeh, A., Thielscher, M. (2018). General Language Evolution in General Game Playing. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03991-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03990-5

  • Online ISBN: 978-3-030-03991-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation