Skip to main content
SpringerLink
Log in

Spatiotemporal Pattern Matching in RoboCup

  • Conference paper
  • First Online:
Multiagent System Technologies (MATES 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9872))

Included in the following conference series:

Abstract

Whereas agent-based models are built on the micro-level, the interesting model output is often observed on the macro-level. In models with agents moving in space this leads to complex movement patterns. We propose a method to describe the simultaneous movement of agents by graphs that encode qualitative spatial relations between object pairs and the change of these relations over time. Movement patterns can then be expressed as graph patterns. We present two approaches to find occurrences of such graph patterns, using a graph database query and using a customized graph algorithm. Based on the example of the RoboCup soccer simulation, we demonstrate the use of our approach to define and find movement patterns in spatial multi-agent systems.

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.

    http://www.zonalmarking.net/.

  2. 2.

    Source code available at http://sourceforge.net/projects/sserver/.

  3. 3.

    neo4j.org.

  4. 4.

    Source code available at http://ai.ustc.edu.cn/2d/.

References

  1. Soccer Simulation League - RoboCup Federation Wiki. http://wiki.robocup.org/wiki/Soccer_Simulation_League

  2. Aiello, M., Pratt-Hartmann, I., van Benthem, J. (eds.): Handbook of Spatial Logics. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  3. Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1:1–1:39 (2008)

    Article  Google Scholar 

  4. Barceló, P., Libkin, L., Reutter, J.L.: Querying graph patterns. In: Proceedings of the 13th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 199–210. ACM (2011)

    Google Scholar 

  5. Bortolussi, L., Nenzi, L.: Specifying and monitoring properties of stochastic spatio-temporal systems in signal temporal logic. In: Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools, pp. 66–73. ICST (2014)

    Google Scholar 

  6. Braun, H.J.: Soccer tactics as science? On ‘Scotch Professors’, a Ukrainian soccer Buddha, and a Catalonian who tries to learn German. J. Int. Comm. Hist. Technol. 19, 216–243 (2013)

    Google Scholar 

  7. Cheng, J.D.: Qualitative spatio-temporal reasoning about movement of mobile agents/objects. In: Proceedings of the 7th International Conference on Machine Learning and Cybernetics, pp. 3341–3346 (2008)

    Google Scholar 

  8. Cohn, A.G., Renz, J.: Qualitative spatial representation and reasoning. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Foundations of Artificial Intelligence, Handbook of Knowledge Representation, vol. 3, pp. 551–596. Elsevier (2008)

    Google Scholar 

  9. De Nicola, R., Katoen, J.P., Latella, D., Loreti, M., Massink, M.: Model checking mobile stochastic logic. Theor. Comput. Sci. 382(1), 42–70 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Düntsch, I.: Relation algebras and their application in temporal and spatial reasoning. Artif. Intell. Rev. 23(4), 315–357 (2005)

    Article  MATH  Google Scholar 

  11. Forbus, K.D.: Qualitative modeling. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Foundations of Artificial Intelligence, Handbook of Knowledge Representation, vol. 3, pp. 361–393. Elsevier (2008)

    Google Scholar 

  12. Frank, A.U.: Qualitative spatial reasoning with cardinal directions. In: Kaindl, H. (ed.) 7. Österreichische Artificial-Intelligence-Tagung/Seventh Austrian Conference on Artificial Intelligence. Informatik-Fachberichte, vol. 287, pp. 157–167. Springer, Heidelberg (1991)

    Google Scholar 

  13. Frank, A.U.: Qualitative spatial reasoning about distances and directions in geographic space. J. Vis. Lang. Comput. 3(4), 343–371 (1992)

    Article  Google Scholar 

  14. Gagné, D., Pang, W., Trudel, A.: A spatio-temporal logic for 2D multi-agent problem domains. Expert Syst. Appl. 12(1), 141–145 (1997)

    Article  Google Scholar 

  15. Gallagher, B.: Matching structure and semantics: a survey on graph-based pattern matching. In: Papers from the 2006 AAAI Fall Symposium, vol. 6, pp. 45–53 (2006)

    Google Scholar 

  16. Grunz, A., Memmert, D., Perl, J.: Tactical pattern recognition in soccer games by means of special self-organizing maps. Hum. Mov. Sci. 31(2), 334–343 (2012)

    Article  Google Scholar 

  17. Gudmundsson, J., van Kreveld, M., Speckmann, B.: Efficient detection of motion patterns in spatio-temporal data sets. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, pp. 250–257. ACM (2004)

    Google Scholar 

  18. Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)

    Article  Google Scholar 

  19. Holzschuher, F., Peinl, R.: Querying a graph database – language selection and performance considerations. J. Comput. Syst. Sci. 82(1), 45–68 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Jouili, S., Vansteenberghe, V.: An empirical comparison of graph databases. In: Proceedings of the 2013 International Conference on Social Computing, pp. 708–715. IEEE Computer Society (2013)

    Google Scholar 

  21. Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. Int. J. Geog. Inf. Sci. 19(6), 639–668 (2005)

    Article  Google Scholar 

  22. Legay, A., Delahaye, B., Bensalem, S.: Statistical model checking: an overview. In: Barringer, H., et al. (eds.) RV 2010. LNCS, vol. 6418, pp. 122–135. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  23. Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(6719), 498–500 (1999)

    Article  Google Scholar 

  24. Noble, J., Silverman, E., Bijak, J., Rossiter, S., Evandrou, M., Bullock, S., Vlachantoni, A., Falkingham, J.: Linked lives: the utility of an agent-based approach to modeling partnership and household formation in the context of social care. In: Proceedings of the Winter Simulation Conference, pp. 93:1–93:12. WSC (2012)

    Google Scholar 

  25. Pârvu, O., Gilbert, D., Heiner, M., Liu, F., Saunders, N., Shaw, S.: Spatial-Temporal modelling and analysis of bacterial colonies with phase variable genes. ACM Trans. Model. Comput. Simul. 25(2), 13:1–13:25 (2015)

    Article  MathSciNet  Google Scholar 

  26. Sakr, M.A., Güting, R.H.: Spatiotemporal pattern queries. GeoInformatica 15(3), 497–540 (2011)

    Article  Google Scholar 

  27. Sakr, M.A., Güting, R.H.: Group spatiotemporal pattern queries. GeoInformatica 18(4), 699–746 (2014)

    Article  Google Scholar 

  28. Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976)

    Article  MathSciNet  Google Scholar 

  29. van Harmelen, F., Lifschitz, V., Porter, B.: Handbook of Knowledge Representation. Elsevier Science, San Diego (2007)

    MATH  Google Scholar 

  30. Wilensky, U.: Modeling natures emergent patterns with multi-agent languages. In: Proceedings of EuroLogo, pp. 1–6. Citeseer (2001)

    Google Scholar 

  31. Zhang, H., Jiang, M., Dai, H., Bai, A., Chen, X.: WrightEagle 2D soccer simulation team description 2014. In: RoboCup (2014)

    Google Scholar 

Download references

Acknowledgments

We would like to thank Roland Ewald, Stefan Leye, and Arne Bittig for their valuable input on the concepts developed in this paper. This research is partly supported by the German Research Foundation (DFG) via the research grant MoSiLLDe (UH-66/15-1).

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Rostock, Albert-Einstein-Straße 22, 18059, Rostock, Germany

    Tom Warnke & Adelinde M. Uhrmacher

Authors
  1. Tom Warnke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adelinde M. Uhrmacher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tom Warnke .

Editor information

Editors and Affiliations

  1. DFKI , Saarbrücken, Germany

    Matthias Klusch

  2. Institute for Computer Science &, University of Duisburg-Essen Institute for Computer Science &, Essen, Nordrhein-Westfalen, Germany

    Rainer Unland

  3. IBM Haifa Research Lab , Haifa, Israel

    Onn Shehory

  4. University of Hamburg VSIS, Hamburg, Hamburg, Germany

    Alexander Pokahr

  5. Distributed Artificial Intelligence, Technical University of Berlin Distributed Artificial Intelligence, Berlin, Berlin, Germany

    Sebastian Ahrndt

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Warnke, T., Uhrmacher, A.M. (2016). Spatiotemporal Pattern Matching in RoboCup. In: Klusch, M., Unland, R., Shehory, O., Pokahr, A., Ahrndt, S. (eds) Multiagent System Technologies. MATES 2016. Lecture Notes in Computer Science(), vol 9872. Springer, Cham. https://doi.org/10.1007/978-3-319-45889-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45889-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45888-5

  • Online ISBN: 978-3-319-45889-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation