Vassilis Papataxiarhis , V.Tsetsos, I.Karali, P.Stamatopoulos, and S.Hadjiefthymiades

1. i-footman: A Knowledge-Based Framework for Football Managers Vassilis Papataxiarhis, V.Tsetsos, I.Karali, P.Stamatopoulos, and S.Hadjiefthymiades vpap@di.uoa.gr Department of Informatics and Telecommunications University of Athens – Greece RuleApps-2009, 21 Sep. 2009, Cottbus

2. Outline Introduction Functionality and Provided Services Application Models and Rules Implementation Simulation Results Conclusions

3. Introduction • What is i-footman? • A decision support system for football managers • Based on Semantic Web technologies • Main Idea • Provide effective tactical guidelines to face an opponent • Restrictions • Empirical/Subjective Knowledge about football • Lack of statistics and ergometric results • No relevant approach (academic or industrial) • Goals • Model the basic knowledge of the domain • Extensibility (in terms of quality and provided services)

4. Knowledge Elicitation • Methodology • Interview 2 domain experts (i.e. football managers) • Questionnaires • Knowledge acquisition about: • the application domain of football • the desired services • the key features of football players and teams • the tactical guidelines that should be supported by the system • Goal: Incorporate the derived knowledge to the rules and application models

5. i-footman Architecture DL-Reasoner i-footman Football Players Ontology Football Teams Ontology reuses reuses reuses Rule Engine user Rules Formation Identification Player Selection Tactical Instructions

6. Functionality Teams Data Formation and Player Selection Rules Football Players Ontology Formation Composition Rules Execution DL-Reasoning Strengths/ Weaknesses Instructions Football Teams Ontology Identification and Tactical Instructions Rules Players Data

7. Ontological Models (1/2) • Expressed in OWL-DL and provide a common vocabulary • Football Players Ontology (FPO) • Some metrics: 71 concepts, 43 object prop., 3 datatype prop., each player instance is described by 22 concept inst. and 9 property inst. • It models: • Position of players • Technical and physical capabilities • Types of players E.g.,fpo:CreativeMiddlefielder≡ (fpo:hasPassing.GoodAbility ⊔fpo:hasPassing.VeryGoodAbility) ⊓ fpo:playsInPosition.Middlefielder • Football Teams Ontology (FTO) • It models main features and types of teams

8. Ontological Models (2/2) • Simplified version of FPO • Key concepts • Player, Position, PlayerFeature • FTO imports FPO • classifies teams according to the features of its players • models tactical instructions allowing the execution of rules

9. Rules • Expressed in terms of SWRL • Motivation: integration of rules and ontologies in the same logical language • Exploit the vocabulary of FPO and FTO • Define more complex concepts and relationships • Constitute the main part of the knowledge acquired by interviewing the experts • Extensible set of rules • Four main categories of rules for the: • identification of team weaknesses/advantages • selection of an appropriate tactical formation • player selection • recommendation of appropriate tactical instructions

10. Rules Examples • Identification Rule • fto:hasStartingPlayer (?t1,?p1) ∧ fto:hasStartingPlayer (?t1,?p2) ∧ fpo:QuickOffensivePlayer (?p1) ∧ fpo:QuickOffensivePlayer (?p2) →fto:dangerousAtCounterAttack (?t1,true). • Formation Rule • fto:myTeamPlaysAgainst(?t1,?t2) ∧ fto:TeamWith3CentralDefenders(?t2) ∧ fto:TeamWith3CentralPlayers(?t2) ∧ fto:TeamWithSideMFs(?t2) ∧ fto:TeamWith2Attackers(?t2) → fto:playsWith3CentralDefenders(?t1, true). • Player Selection Rule • fto:myTeamPlaysAgainst(?t1,?t2) ∧ fpo:playsWith1Striker(?t1) ∧ fpo:GoodStriker (?p1) ∧ fpo:isMemberOf(?p1,?t1) → fpo:isSuggestedTo(?p1,?t1). • Tactical Instruction Rule • fto:myTeamPlaysAgainst(?t1,?t2) ∧ fto:TeamWithNoBacks(?t2) ∧ fto:TeamWithWingers(?t1) → fto:shouldAttackFromTheWings(?t1, true).

11. Implementation Details • Web Ontology Language (OWL-DL) • Semantic Web Rule Language (SWRL) • Pellet Reasoner (v. 1.5.1) • Jess Rule Engine • Protégé SWRL Jess Tab • Protégé OWL API • SPARQL • Jena2 inference module – Jena API • Apache Tomcat

12. Evaluation (1/2) • Simulation of football matches in 2 platforms with and without the intervention of i-footman • 2 Scenarios • Teams with similar ratings • i-footman controls a weaker team • 40 games in each platform (80 games in total) • Scenario 1

13. Evaluation (2/2) • Scenario 2 • No significant improvement when controlling a better team • Performance Evaluation Average Response Time = 7740ms

14. Conclusion • Contributions • A knowledge-based system based on SW technologies • An extensible framework for football managers • FPO, FTO ontologies • Open Issues • Integrated reasoning module for handling rules and ontologies seamlessly • Real data are not available • Future Work • Automated ontology creation by statistics and ergometric data • Learning rules by historical data stemmed from simulations without the intervention of i-footman • Adoption of fuzzy approaches to deal with uncertainty Inferred Knowledge Ontological Reasoning Rules Execution Inferred Knowledge

15. Thank you! http://www.di.uoa.gr/~vpap/i-footman