ifootman A KnowledgeBased Framework for Football Managers Vassilis

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 Rule. Apps-2009, 21 Sep. 2009, Cottbus

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

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)

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

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

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

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: Creative. Middlefielder≡ (fpo: has. Passing. Good. Ability ⊔ fpo: has. Passing. Very. Good. Ability) ⊓ fpo: plays. In. Position. Middlefielder Football Teams Ontology (FTO) ◦ It models main features and types of teams

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

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

Rules Examples Identification Rule ◦ fto: has. Starting. Player (? t 1, ? p 1) ∧ fto: has. Starting. Player (? t 1, ? p 2) ∧ fpo: Quick. Offensive. Player (? p 1) ∧ fpo: Quick. Offensive. Player (? p 2) → fto: dangerous. At. Counter. Attack (? t 1, true). Formation Rule ◦ fto: my. Team. Plays. Against(? t 1, ? t 2) ∧ fto: Team. With 3 Central. Defenders(? t 2) ∧ fto: Team. With 3 Central. Players(? t 2) ∧ fto: Team. With. Side. MFs(? t 2) ∧ fto: Team. With 2 Attackers(? t 2) → fto: plays. With 3 Central. Defenders(? t 1, true). Player Selection Rule ◦ fto: my. Team. Plays. Against(? t 1, ? t 2) ∧ fpo: plays. With 1 Striker(? t 1) ∧ fpo: Good. Striker (? p 1) ∧ fpo: is. Member. Of(? p 1, ? t 1) → fpo: is. Suggested. To(? p 1, ? t 1). Tactical Instruction Rule ◦ fto: my. Team. Plays. Against(? t 1, ? t 2) ∧ fto: Team. With. No. Backs(? t 2) ∧ fto: Team. With. Wingers(? t 1) → fto: should. Attack. From. The. Wings(? t 1, true).

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 Jena 2 inference module – Jena API Apache Tomcat

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

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

Conclusion Contributions ◦ A knowledge-based system based on SW technologies ◦ An extensible framework for football managers ◦ FPO, FTO ontologies Open Issues Inferred ◦ Integrated reasoning module for handling rules and ontologies seamlessly ◦ Real data are not available Future Work Knowledge Rules Execution Ontological Reasoning Inferred Knowledge ◦ 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

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