1. 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 i-footman: A Knowledge-Based Framework for Football Managers

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

3.  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) Introduction

4.  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 Knowledge Elicitation

5. i-footman Rule Engine Football Players Ontology IdentificationFormation DL-Reasoner Football Teams Ontology Rules Player SelectionTactical Instructions user reuses i-footman Architecture

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

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.  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 Rules

10.  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). Rules Examples

11.  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 Implementation Details

12.  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 Evaluation(1/2)

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

14.  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 Conclusion Ontological Reasoning Inferred Knowledge Rules Execution Inferred Knowledge

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