vassilis papataxiarhis , v.tsetsos, i.karali, p.stamatopoulos, and s.hadjiefthymiades
DESCRIPTION
i-footman: A Knowledge-Based Framework for Football Managers. Vassilis Papataxiarhis , V.Tsetsos, I.Karali, P.Stamatopoulos, and S.Hadjiefthymiades [email protected] Department of Informatics and Telecommunications University of Athens – Greece RuleApps-2009, 21 Sep. 2009, Cottbus. Outline. - PowerPoint PPT PresentationTRANSCRIPT
Vassilis Papataxiarhis, V.Tsetsos, I.Karali, P.Stamatopoulos, and S.Hadjiefthymiades
Department of Informatics and TelecommunicationsUniversity of Athens – Greece
RuleApps-2009, 21 Sep. 2009, CottbusRuleApps-2009, 21 Sep. 2009, Cottbus
i-footman: A Knowledge-Based Framework for Football
Managers
Introduction
Functionality and Provided Services
Application Models and Rules
Implementation
Simulation Results
Conclusions
Outline
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
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
i-footman
Rule Engine
Football Players Ontology
IdentificationFormation
DL-Reasoner
Football TeamsOntology
Rules
Player Selection Tactical Instructions
user
reuses
reusesreuses
i-footman Architecture
Football Players
Ontology
DL-ReasoningRules
Execution
Formation andPlayer Selection
Rules
Identification andTactical
Instructions Rules
Formation
Composition
Strengths/Weaknesses
InstructionsFootball Teams
Ontology
Players Data
Teams Data
Functionality
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
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
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
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
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
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)
Barcelona FC vs. Real Madrid FC (match results)
14
10
1615
17
8
02
46
810
1214
1618
Wins Draws Losses
CPU
i-footman
Barcelona FC vs. Real Madrid FC (goals)
45
51
61
36
0
10
20
30
40
50
60
70
Goals + Goals -
CPU
i-footman
Scenario 2
No significant improvement when controlling a better team
Performance Evaluation
Evaluation (2/2)
Average Response Time = 7740ms
Olympiacos SFP vs. Real Madrid FC (match results)
3
11
26
5
1817
0
5
10
15
20
25
30
Wins Draws Losses
CPU
i-footman
Olympiacos SFP vs. Real Madrid FC (goals)
23
31
87
62
0
10
20
30
40
50
60
70
80
90
100
Goals + Goals -
CPU
i-footman
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 ontologiesseamlessly
◦ 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
OntologicalReasoning
InferredKnowledge
RulesExecution
InferredKnowledge
Thank you!Thank you!
http://www.di.uoa.gr/~vpap/i-footman