nach oben

Soft Computing

Erschienen in:

01.05.2013 | Methodologies and Application

An automatic approach to extract goal plans from soccer simulated matches

verfasst von: Fernando Almeida, Pedro Henriques Abreu, Nuno Lau, Luís Paulo Reis

Erschienen in: Soft Computing | Ausgabe 5/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Soccer is a competitive and collective sport in which teammates try to combine the execution of basic actions (cooperative behavior) to lead their team to more advantageous situations. The ability to recognize, extract and reproduce such behaviors can prove useful to improve the performance of a team in future matches. This work describes a methodology for achieving just that makes use of a plan definition language to abstract the representation of relevant behaviors in order to promote their reuse. Experiments were conducted based on a set of game log files generated by the Soccer Server simulator which supports the RoboCup 2D simulated robotic soccer league. The effectiveness of the proposed approach was verified by focusing primarily on the analysis of behaviors which started from set-pieces and led to the scoring of goals while the ball possession was kept. One of the results obtained showed that a significant part of the total goals scored was based on this type of behaviors, demonstrating the potential of conducting this analysis. Other results allowed us to assess the complexity of these behaviors and infer meaningful guidelines to consider when defining plans from scratch. Some possible extensions to this work include assessing which plans have the ability to maximize the creation of goal opportunities by countering the opponent’s team strategy and how the effectiveness of plans can be improved using optimization techniques.

Vorheriger Artikel Neighborhood field for cooperative optimization

Nächster Artikel Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Game situation in which the ball is returned to open play after a stoppage due to the ball going out-of-bounds or a call made by the referee. In a soccer match this includes kick-offs, goal-kicks, throw-ins, corner-kicks and free-kicks. In this work, the latter is also used to refer to indirect free-kicks.

More information available online at http://sourceforge.net/projects/sserver/.

More information available online at http://ne.cs.uec.ac.jp/*koji/SoccerScope2/index.htm.

This cooperative behavior was exhibited by the WrightEagle team in the RoboCup 2010 quarter finals game against opuCI_2D at cycle 4551 and lasted 27 cycles.

Abreu PH, Faria M, Reis LP, Garganta J (2010a) Knowledge representation in soccer domain: an ontology development. In: Proceedings of the 2010 Iberian conference on information systems and technologies, pp 1–6

Abreu PH, Moura J, Silva DC, Reis LP, Garganta J (2010b) Football Scientia—an automated tool for professional soccer coaches. In: Proceedings of the 2010 IEEE international conference on cybernetics and intelligent systems (CIS 2010), June 28–30, 2010, Singapore. IEEE, pp 126–131

Abreu PH, Vinhas V, Mendes P, Reis LP, Garganta J (2010c) Real-time wireless location and tracking system with motion pattern detection. InTech, Robot localization and map building, pp 467–492

Abreu PH, Moura J, Silva DC, Reis LP, Garganta J (2011) Performance analysis in soccer: a Cartesian coordinates based approach using RoboCup data. In: Soft computing—a fusion of foundations, methodologies and applications, pp 1–15

Abreu PH, Mendes-Moreira J, Costa I, Castelão D, Reis LP, Garganta J (2012) Human versus virtual robotics soccer: a technical analysis. Eur J Sport Sci 12(1):26–35CrossRef

Aler R, Valls JM, Camacho D, Lopez A (2009) Programming Robosoccer agents by modeling human behavior. Expert Syst Appl 36(2, Part 1):1850–1859

Bauckhage C, Thurau C, Sagerer G (2003) Learning human-like opponent behavior for interactive computer games. In: DAGM symposium for pattern recognition, pp 148–155

Carling C, Williams A, Reilly T (2007) Handbook of soccer match analysis: a systematic approach to improving performance, 2nd edn. Routledge

de Dios Bulos R, Dulalia C, Go P, Tan P, Uy M (2005) A data mining approach in opponent modeling. In: Zhang S, Jarvis R (eds) AI 2005: advances in artificial intelligence. LNCS, vol 3809. Springer, Berlin, pp 993–996

Druecker C, Huebner S, Neumann H, Schmidt E, Visser U, Weland H (2000) Virtualweder: using the online-coach to change team formations. Technical report, University of Bremen

Fathzadeh R, Mokhtari V, Mousakhani M, Mahmoudi F (2006) Mining Opponent behavior: a champion of RoboCup coach competition. In: Robotics symposium, 2006. LARS ’06. IEEE 3rd Latin American, pp 80–83

Fathzadeh R, Mokhtari V, Kangavari MR (2008) Opponent provocation and behavior classification: a machine learning approach. In: RoboCup 2007: Robot Soccer World Cup XI. Springer, Berlin, pp 540–547

Gonzalez I, Abreu PH, Reis LP (2008) Using a datawarehouse to extract knowledge from robocup teams. In: Proceedings of the 2008 international conference on enterprise and information systems, pp 511–514

Iglesias JA, Fernández JA, Villena IR, Ledezma A, Sanchis A (2009a) The winning advantage: using opponent models in robot soccer. In: Proceedings of the 10th international conference on intelligent data engineering and automated learning, IDEAL’09. Springer, Berlin, pp 485–493

Iglesias JA, Ledezma A, Sanchis A (2009) CAOS Coach 2006 simulation team: an opponent modelling approach. Comput Inf 28(1):57–80

Kok JR, Spaan MTJ, Vlassis N (2002) An approach to noncommunicative multiagent coordination in continuous domains. In: Wiering M (ed) Proceedings of the 12th Belgian-Dutch conference on machine learning, Utrecht, The Netherlands, pp 46–52

Kok JR, Spaan MTJ, Vlassis N (2003) Multi-robot decision making using coordination graphs. In: de Almeida AT, Nunes U (eds) Proceedings of the 11th international conference on advanced robotics, Coimbra, Portugal, pp 1124–1129

Kontes G, Lagoudakis M (2007) Coordinated team play in the four-legged RoboCup league. In: Proceedings of the 19th IEEE conference on tools with artificial intelligence (ICTAI), vol 1. IEEE Computer Society, Washington, pp 109–116

Kuhlmann G, Stone P, Lallinger J (2005) The UT Austin Villa 2003 champion simulator coach: a machine learning approach. In: Nardi D, Riedmiller M, Sammut C, Santos-Victor J (eds) RoboCup 2004: Robot Soccer World Cup VIII. LNCS, vol 3276. Springer, Berlin, pp 636–644

Ledezma A, Aler R, Sanchis A, Borrajo D (2005) Predicting opponent actions by observation. In: Nardi D, Riedmiller M, Sammut C, Santos-Victor J (eds) RoboCup 2004: Robot Soccer World Cup VIII, The RoboCup Federation. LNCS, vol 3276. Springer, Lisbon, pp 286–296

Ledezma A, Aler R, Sanchis A, Borrajo D (2009) OMBO: an opponent modeling approach. AI Community 22(1):21–35MathSciNetMATH

Mota L, Lau N, Reis LP (2010) Co-ordination in RoboCup’s 2D simulation league: setplays as flexible, multi-robot plans. In: 2010 IEEE Conference on robotics automation and mechatronics (RAM), pp 362 –367

Noda I, Matsubara H, Hiraki K, Frank I (1998) Soccer server: a tool for research on multiagent systems. Appl Artif Intell 12(2–3):233–250CrossRef

Obst O (2006) Using a planner for coordination of multiagent team behavior. In: Bordini RH, Dastani M, Dix JCBr, Seghrouchni AE (eds) Programming multi-agent systems: 3rd international workshop, ProMAS 2005, revised and invited papers, vol 3862. Springer, Berlin, pp 90–100

Obst O, Boedecker J (2006) Flexible coordination of multiagent team behavior using HTN planning. In: Noda I, Jacoff A, Bredenfeld A, Takahashi Y (eds) RoboCup 2005: Robot Soccer World Cup IX. Springer, Berlin, pp 521–528

Palamara PF, Ziparo VA, Iocchi L, Nardi D, Lima P (2009) Teamwork design based on Petri Net plans. In: Iocchi L, Matsubara H, Weitzenfeld A, Zhou C (eds) RoboCup 2008: Robot Soccer World Cup Xii, vol 5399. Springer, Berlin, pp 200–211

Portela J, Abreu PH, Reis LP, Oliveira EC, Garganta J (2010) An intelligent framework for automatic event detection in robotic soccer games: an auxiliar tool to help coaches improve their teams performance. In: Proceedings of the 2010 international conference on enterprise information systems, pp 244–249

Rad AA, Qaragozlou N, Zaheri M (2004) Scenario-based teamworking, how to learn, create, and teach complex plans? In: Polani D, Browning B, Bonarini A, Yoshida K (eds) RoboCup 2003: Robot Soccer World Cup VII, vol 3020. Springer, Berlin, pp 137–144

Riley P, Veloso M (2001) Coaching a simulated soccer team by opponent model recognition. In: Proceedings of the 5th international conference on autonomous agents, AGENTS ’01. ACM, New York, pp 155–156

Riley P, Veloso M (2006) Coach planning with opponent models for distributed execution. Auton Agent Multi-Agent Syst 13:293–325CrossRef

Riley P, Veloso M, Kaminka G (2002) Towards any-team coaching in adversarial domains. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 3, AAMAS ’02. ACM, New York, pp 1145–1146

Steffens T (2004a) Adapting similarity-measures to agent-types in opponent-modelling. In: Bauer M, Gmytrasiewicz P, Kaminka GA, Pynadath DV (eds) Workshop on modeling other agents from observations at AAMAS, pp 125–128

Steffens T (2004b) Feature-based declarative opponent-modelling. In: Polani D, Browning B, Bonarini A, Yoshida K (eds) RoboCup 2003: Robot Soccer World Cup VII. LNCS, vol 3020. Springer, Berlin, pp 125–136

Steffens T (2005) Knowledge-intensive similarity-based opponent modelling. In: Aha DW (ed) Proceedings of the IJCAI workshop on representation, reasoning, and learning in computer games

Stone P, Riley P, Veloso M (2000) Defining and using ideal teammate and opponent agent models. In: Proceedings of the seventeenth national conference on artificial intelligence and twelfth conference on innovative applications of artificial intelligence. AAAI, San Francisco, pp 1040–1045

Ziparo VA, Iocchi L, Nardi D, Palamara PF, Costelha H (2008) Petri net plans: a formal model for representation and execution of multi-robot plans. In: Proceedings of the 7th international joint conference on international foundation for autonomous agents and multiagent systems, AAMAS’08, Richland, vol 1, pp 79–86

Titel: An automatic approach to extract goal plans from soccer simulated matches
verfasst von: Fernando Almeida
Pedro Henriques Abreu
Nuno Lau
Luís Paulo Reis
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0952-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2013

Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

Large data sets classification using convex–concave hull and support vector machine

Robust H ∞ output feedback control with pole placement constraints for uncertain discrete-time fuzzy systems

An examination of different fitness and novelty based selection methods for the evolution of neural networks

Dynamic characteristic of a multiple chaotic neural network and its application

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization