Skip to main content

Solving Motion and Action Planning for a Cooperative Agent Problem Using Geometry Friends

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Included in the following conference series:

Abstract

In this paper we discuss the development of agents for the Geometry Friends game, which poses simultaneously problems of planning and motion control in an physics-based puzzle and platform 2D world. The game is used in a competition, held yearly, that challenges participants to solve single player and cooperative levels. Our work addresses the two. The approach followed uses Rapidly-Exploring Random Trees with strategies to accelerate the search. When comparing with other agents on the competition, our results show that our agents can solve the single player challenges without overspecialization and are also promising for the cooperative levels with either agent-agent and human-agent players.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Bradshaw, J., Feltovich, P., Matthew, J.: Human-agent interaction. In: Boy, G. (ed.) Handbook of Human-Machine Interaction, pp. 283–302. Ashgate Publishing Ltd., Farnham (2011)

    Google Scholar 

  2. Bruce, J., Veloso, M.: Real-time randomized path planning for robot navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2383–2388 (2002). https://doi.org/10.1109/IRDS.2002.1041624

  3. Bruce, J., Veloso, M.: Real-time randomized motion planning for multiple domains. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 532–539. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74024-7_55

    Chapter  Google Scholar 

  4. Hoffman, G., Breazeal, C.: Collaboration in human-robot teams. In: Proceedings of the AIAA 1st Intelligent Systems Technical Conference, pp. 1–18 (2004). https://doi.org/10.2514/6.2004-6434

  5. Kraus, S.: Human-agent decision-making: combining theory and practice. Electron. Proc. Theoret. Comput. Sci. 215, 13–27 (2016). https://doi.org/10.4204/EPTCS.215.2

    Article  MathSciNet  Google Scholar 

  6. LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Technical report, TR 98-11 (1998). https://doi.org/10.1.1.35.1853

  7. LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: progress and prospects, pp. 293–308 (2000) https://doi.org/10.1017/CBO9781107415324.004

  8. Prada, R., Lopes, P., Catarino, J., Quiterio, J., Melo, F.S.: The geometry friends game AI competition. In: Proceedings of 2015 IEEE Conference on Computational Intelligence and Games, CIG 2015, pp. 431–438 (2015)

    Google Scholar 

  9. Soares, R., Leal, F., Prada, R., Melo, F.: Rapidly-exploring random tree approach for geometry friends. In: Proceedings of 1st International Joint Conference of DiGRA and FDG (2016)

    Google Scholar 

  10. Van Wissen, A., Gal, Y., Kamphorst, B.A., Dignum, M.V.: Human-agent teamwork in dynamic environments. Comput. Hum. Behav. 28(1), 23–33 (2012). https://doi.org/10.1016/j.chb.2011.08.006

    Article  Google Scholar 

  11. Weibel, D., Wissmath, B., Habegger, S., Steiner, Y., Groner, R.: Playing online games against computer- vs. human-controlled opponents effects on presence, flow, and enjoyment. Comput. Hum. Behav. 24(5), 2274–2291 (2008). https://doi.org/10.1016/j.chb.2007.11.002

    Article  Google Scholar 

  12. Zickler, S., Veloso, M.: Efficient physics-based planning: sampling search via non-deterministic tactics and skills. In: The 8th International Conference on Autonomous Agents and Multiagent Systems, pp. 27–34 (2009)

    Google Scholar 

  13. Zickler, S., Veloso, M.: Variable level-of-detail motion planning in environments with poorly predictable bodies. Frontiers Artif. Intell. Appl. 215, 189–194 (2010). https://doi.org/10.3233/978-1-60750-606-5-189

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT-UID/CEC/50021/2019) and through the project AMIGOS (PTDC/EEISII/7174/2014).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ana Salta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salta, A., Prada, R., Melo, F. (2019). Solving Motion and Action Planning for a Cooperative Agent Problem Using Geometry Friends. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30241-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics