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.
Keywords
- Graph Pattern Matching
- Time Nodal Points
- Jointly Exhaustive And Pairwise Disjoint (JEPD)
- Football Analysis
- Football Tactics
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
Source code available at http://sourceforge.net/projects/sserver/.
- 3.
- 4.
Source code available at http://ai.ustc.edu.cn/2d/.
References
Soccer Simulation League - RoboCup Federation Wiki. http://wiki.robocup.org/wiki/Soccer_Simulation_League
Aiello, M., Pratt-Hartmann, I., van Benthem, J. (eds.): Handbook of Spatial Logics. Springer, Heidelberg (2007)
Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1:1–1:39 (2008)
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)
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)
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)
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)
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)
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)
Düntsch, I.: Relation algebras and their application in temporal and spatial reasoning. Artif. Intell. Rev. 23(4), 315–357 (2005)
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)
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)
Frank, A.U.: Qualitative spatial reasoning about distances and directions in geographic space. J. Vis. Lang. Comput. 3(4), 343–371 (1992)
Gagné, D., Pang, W., Trudel, A.: A spatio-temporal logic for 2D multi-agent problem domains. Expert Syst. Appl. 12(1), 141–145 (1997)
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)
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)
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)
Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)
Holzschuher, F., Peinl, R.: Querying a graph database – language selection and performance considerations. J. Comput. Syst. Sci. 82(1), 45–68 (2016)
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)
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)
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)
Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(6719), 498–500 (1999)
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)
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)
Sakr, M.A., Güting, R.H.: Spatiotemporal pattern queries. GeoInformatica 15(3), 497–540 (2011)
Sakr, M.A., Güting, R.H.: Group spatiotemporal pattern queries. GeoInformatica 18(4), 699–746 (2014)
Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976)
van Harmelen, F., Lifschitz, V., Porter, B.: Handbook of Knowledge Representation. Elsevier Science, San Diego (2007)
Wilensky, U.: Modeling natures emergent patterns with multi-agent languages. In: Proceedings of EuroLogo, pp. 1–6. Citeseer (2001)
Zhang, H., Jiang, M., Dai, H., Bai, A., Chen, X.: WrightEagle 2D soccer simulation team description 2014. In: RoboCup (2014)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)