integration of symbolic and connectionist ai techniques fine...engineering systems, 11th...
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Integration of Symbolic and ConnectionistAI techniques
Decision Support Systems for biochemical processes
Davide Sottara
Seminari III anno dottorato - XXII ciclo
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Curriculum
Tutor:Prof. P. Mello
Co-Tutor:Ing. L. Luccarini
Cooperations :ENEA - ACS PROT IDR WaterResource Management Section(Jan 07 - Dec 09)
University of Newcastle / JBoss(Feb 09 - Jun 09)
Other:Track Co-Chair at RULEML09 “Rulesand Uncertainty”
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Publications
Journal Papers
G. L. Bragadin, G. Colombini, L. Luccarini, M. Mancini, P. Mello, M. Montali,and D. Sottara.Formal verification of wastewater treatment processes using events detected fromcontinuous signals by means of artificial neural networks. Case study: SBR plant.Environmental Modelling and Software (IF 2.659).Article in Press.
P. Mello, M. Proctor, and D. Sottara.A configurable RETE-OO engine for reasoning with different types of imperfectinformation.IEEE Transactions on Knowledge and Data Engineering (TKDE) - Special Issueon Rule Representation, Interchange and Reasoning in Distributed,Heterogeneous Environments (IF 2.236).Article in Press.
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Conference Acts I
Sottara D., P.Mello, L.Luccarini, and G.Colombini.
Controllo e gestione intelligente degli impianti di depurazione.In Europa del Recupero : le ricerche, le tecnologie, gli strumenti e i casi studio per una cultura dellaresponsabilità ambientale, pages 156 – 161, S.Arcangelo di Romagna (RN) – ITA, 5-8 Novembre 2008.Maggioli Editore (ITALY).
D.Sottara, L.Luccarini, and P.Mello.
Strumenti di IA per il controllo e la diagnosi dei processi biologici negli impianti a fanghi attivi.In Europa del recupero : le ricerche, le tecnologie, gli strumenti e i casi studio per una cultura dellaresponsabilità ambientale, pages 150 – 155, S.Arcangelo di Romagna (RN) – ITA, 5-8 Novembre 2008.Maggioli Editore (ITALY).
L. Luccarini, P. Mello, D. Sottara, and A. Spagni.
Artificial Intelligence based rules for event recognition and control applied to SBR systems.In Conference Proceedings of the 4th Sequencing Batch Reactor Conference, pages 155 – 158, ROMA –ITA, 7-10 April, 2008. s.n.
M. Nickles and D. Sottara.
Approaches to Uncertain or Imprecise Rules - A survey.In G. Governatori, J. Hall, and A. Paschke, editors, Rule Interchange and Applications, InternationalSymposium, RuleML 2009, Las Vegas, Nevada, USA, November 5-7, 2009. Proceedings, volume 5858 ofLecture Notes in Computer Science, pages 323–336. Springer, 2009.
D. Sottara and P. Mello.
Modelling radial basis functions with rational logic rules.In E. Corchado, A. Abraham, and W. Pedrycz, editors, Hybrid Artificial Intelligence Systems, ThirdInternational Workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008. Proceedings, volume 5271 ofLecture Notes in Computer Science, pages 337–344. Springer, 2008.
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Conference Acts II
D. Sottara, L. Luccarini, P. Mello, S. Grilli, M. Mancini, and G.L. Bragadin.
Tecniche di intelligenza artificiale per la gestione e il controllo di impianti di depurazione. caso di studio:SBR in scala pilota alimentato con refluo reale.In Luciano Morselli, editor, Ambiente: tecnologie, controlli e certificazioni per il recupero e la valorizzazionedi materiali ed energie. ECOMONDO X Fiera Internazionale del Recupero di Materia ed Energia e delloSviluppo Sostenibile. Rimini. 8-11 novembre 2006, volume 1, pages 106 – 111. Maggioli Editore (ITALY),2006.
D. Sottara, L. Luccarini, and P. Mello.
AI techniques for Waste Water Treatment Plant control. Case study: Denitrification in a pilot-scale SBR.In B. Apolloni, R. J. Howlett, and L. C. Jain, editors, Knowledge-Based Intelligent Information andEngineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks,Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part I, volume 4692 of Lecture Notes inComputer Science, pages 639–646. Springer, 2007.
D. Sottara, P. Mello, and M. Proctor.
Adding uncertainty to a RETE-OO inference engine.In N. Bassiliades, G. Governatori, and A. Paschke, editors, Rule Representation, Interchange and Reasoningon the Web, International Symposium, RuleML 2008, Orlando, FL, USA, October 30-31, 2008.Proceedings, volume 5321 of Lecture Notes in Computer Science, pages 104–118. Springer, 2008.
D. Sottara, G. Colombini, L. Luccarini, and P. Mello.
A Pool of Experts to evaluate the evolution of biological processes in SBR plants.In E. Corchado, X. Wu, E. Oja, Á. Herrero, and B. Baruque, editors, Hybrid Artificial Intelligence Systems,4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, volume 5572of Lecture Notes in Computer Science, pages 368–375. Springer, 2009.
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Conference Acts III
D. Sottara, L. Luccarini, G.L. Bragadin, M.L. Mancini, P. Mello, and M. Montali.
Process quality assessment in automatic management of wastewater treatment plants using formalverification.In International Symposium on Sanitary and Environmental Engineering-SIDISA 08 -Proceedings, volume 1,pages 152/1 – 152/8, ROMA – ITA, 24-27 june 2008 2009. ANDIS.
D. Sottara, A. Manservisi, P. Mello, G. Colombini, and L. Luccarini.
A CEP-based SOA for the management of wastewater treatment plants.In EESMS 2009. IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, 2009.Proceedings, pages 58–65, 25/09/ 2009.
D. Sottara, P. Mello, L. Luccarini, G. Colombini, and A. Manservisi.
Controllo intelligente in linea per una gestione efficiente e sostenibile degli impianti di trattamento reflui.Caso di studio: SBR in scala pilota.In Ecodesign per il pianeta: soluzioni per un ambiente pulito e per una nuova economia, pages 655 – 660,S.Arcangelo di Romagna (RN) – ITA, 28-31 Ottobre 2009. Maggioli Editore (ITALY).
D. Sottara, P. Mello, and M. Proctor.
Towards modelling defeasible reasoning with imperfection in production rule systems.In G. Governatori, J. Hall, and A. Paschke, editors, Rule Interchange and Applications, InternationalSymposium, RuleML 2009, Las Vegas, Nevada, USA, November 5-7, 2009. Proceedings, volume 5858 ofLecture Notes in Computer Science, pages 345–352. Springer, 2009.
N. Wulff and D. Sottara.
Fuzzy reasoning with a RETE-OO Rule Engine.In G. Governatori, J. Hall, and A. Paschke, editors, Rule Interchange and Applications, InternationalSymposium, RuleML 2009, Las Vegas, Nevada, USA, November 5-7, 2009. Proceedings, volume 5858 ofLecture Notes in Computer Science, pages 337–344. Springer, 2009.
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Case Study : Water Treatment
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Case Study : Water Treatment
Sequencing Batch Reactors
Single treatment tank
Cyclic process : Reactions sequential in time
pH, redox potential orp, dissolved oxygen DO probes
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Case Study : Water Treatment
Intelligent Management of Complex Systems
Control
Process Optimization
Greater efficiencyMoney/Energy savings
Diagnosis
Anomaly Prevention
Fault Isolation
Automatic Intervention
Plant
Probes Actuators
Detection Reaction
Diagnosis Prevention
Support Intervention
Operator
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Case Study : Water Treatment
Reaction Completion
Change in signal trends are correlated to completed reactions
0 50 100 150 200 250 300 350−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
Load Anox Aero Set
Draw
Idle
Denitrification Nitrification
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
A few considerations...
The problem is complex
Models are not applicable
Decision Support Systems are more suitable
No single AI technology is optimal
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
A few considerations...
The problem is complex
Models are not applicable
Decision Support Systems are more suitable
No single AI technology is optimal
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
A few considerations...
The problem is complex
Models are not applicable
Decision Support Systems are more suitable
No single AI technology is optimal
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
A few considerations...
The problem is complex
Models are not applicable
Decision Support Systems are more suitable
No single AI technology is optimal
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
Integrated Intelligent Remote Control
PlantProbes Actuators
PID
ControllerDatabase
User Interface
DSS
Integrated RemoteControl System
Combines differenttechnologies
Remote Access
Diagnosis & FaultDetection
Optimal Set-pointControl
Operator Support
Benefits
Full KB system
Reactive / Proactive
Limitations
Monolithic architecture
Coordination?
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
State of the Art
Integrated Intelligent Remote Control
PlantProbes Actuators
PID
ControllerDatabase
User Interface
DSSDN FRO
Analysis Techniques
Different approaches
Data Mining
Neural Networks
Fuzzy Logic
Rule BasedSystems
Ontologies
Benefits
Full KB system
Reactive / Proactive
Limitations
Monolithic architecture
Coordination?
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Complex Event-Driven SOAs
CED-SOA
A Service-Oriented Architecture for Complex Event Processing
Services may interact producing or consuming events
Looser couplingReactiveness
Services aggregate events
Implementation is hidden
The middleware delivers events
Producers need not know Consumers, if any
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Complex Event-Driven SOAs
CED-SOA
A Service-Oriented Architecture for Complex Event Processing
Services may interact producing or consuming events
Looser couplingReactiveness
Services aggregate events
Implementation is hidden
The middleware delivers events
Producers need not know Consumers, if any
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Complex Event-Driven SOAs
CED-SOA
A Service-Oriented Architecture for Complex Event Processing
Services may interact producing or consuming events
Looser couplingReactiveness
Services aggregate events
Implementation is hidden
The middleware delivers events
Producers need not know Consumers, if any
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Complex Event-Driven SOAs
CED-SOA
A Service-Oriented Architecture for Complex Event Processing
Services may interact producing or consuming events
Looser couplingReactiveness
Services aggregate events
Implementation is hidden
The middleware delivers events
Producers need not know Consumers, if any
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Ctrl + PIDDatabase
User Interface
DSSDN FRO
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Ctrl + PIDDatabase
User Interface
DSS
DN FRO
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Ctrl + PIDDatabase
User Interface
DSS
D N F R O
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Ctrl + PIDDatabase
User Interface
DSS
ORFN
D
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Database
User Interface
DSS
ORFN
D
Controller
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
Database
User Interface
DSS
ORFN
D
ControllerAcquisition
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
ORFN
D
ControllerAcquisition
Store
DW
I/O
UI
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
ORFN
D
ControllerAcquisition
Store
DW
I/O
UISecurityAdmin
Registry . . .
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
ORFN
D
ControllerAcquisition
Store
DW
I/O
UISecurityAdmin
Registry . . .
Scheduler
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
ORFN
D
ControllerAcquisition
Store
DW
I/O
UISecurityAdmin
Registry . . .
Scheduler
Rule
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Towards Complex Achitectures
PlantProbes Actuators
ORFN
D
ControllerAcquisition
Store
DW
I/O
UISecurityAdmin
Registry . . .
Scheduler
Rule
Router
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Event-Processing Networks : (Loose) Interactions
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Raw
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Sample
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Trend
Stage
Estimate
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Switch
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Phase
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Proposed Architecture
Events : A typical scenario
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Switch
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
AI Modules
Chart Statistics Scheduler
Predict
Probes Denoise Analysis Policy Control Actuators
Trace
Storage Router
Num NumR
SOMR
SOMFFR
P R
R
R
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Hybrid Systems
Combine benefits of Soft and Hard Computing
Hard Computing (HCS)
Encode Knowledge
Self-Explanatory
Reason
Soft Computing (SCS)
Learn
Flexible
Evaluate
Complementary
Problem : Integration
The output of SCS is Uncertain and unsuitable for HCS
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Hybrid Systems
Combine benefits of Soft and Hard Computing
Hard Computing (HCS)
Encode Knowledge
Self-Explanatory
Reason
Soft Computing (SCS)
Learn
Flexible
Evaluate
Complementary
Problem : Integration
The output of SCS is Uncertain and unsuitable for HCS
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
An Ontology for Uncertainty (W3C)
Uncertainty
Nature Derivation Type Model
AleatoryEpisthemic
SubjectiveObjective
IncompletenessVagueness
InconsistencyRandomness
Ambiguity
FuzzySetsRoughSets
RandomSetsBelief
Probability
more. . .
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
An Ontology for Uncertainty (W3C)
Uncertainty
Nature Derivation Type Model
AleatoryEpisthemic
SubjectiveObjective
IncompletenessVagueness
InconsistencyRandomness
Ambiguity
FuzzySetsRoughSets
RandomSetsBelief
Probability
more. . .
Uncertainty / Confidence Factors
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
An Ontology for Uncertainty (W3C)
Uncertainty
Nature Derivation Type Model
AleatoryEpisthemic
SubjectiveObjective
IncompletenessVagueness
InconsistencyRandomness
Ambiguity
FuzzySetsRoughSets
RandomSetsBelief
Probability
more. . .
Uncertainty / Frequentist Probability
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
An Ontology for Uncertainty (W3C)
Uncertainty
Nature Derivation Type Model
AleatoryEpisthemic
SubjectiveObjective
IncompletenessVagueness
InconsistencyRandomness
Ambiguity
FuzzySetsRoughSets
RandomSetsBelief
Probability
more. . .
Uncertainty / Bayesian Probability
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
An Ontology for Uncertainty (W3C)
Uncertainty
Nature Derivation Type Model
AleatoryEpisthemic
SubjectiveObjective
IncompletenessVagueness
InconsistencyRandomness
Ambiguity
FuzzySetsRoughSets
RandomSetsBelief
Probability
more. . .
Vagueness / Fuzzy Logic
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Rule examples : Analysis
r u l e "Step Up"// f u z z y r u l e
when$ f : F e a t u r e s ( d e l t a T i s "short"
and d e l t a Y i s "high"and l e f t D e r i s "flat"and cenDer i s "steepPositive" )
thenTrendChange t c = new TrendChange ( $f ,
"step_up" ,d r o o l s . getConsequenceDegree ( ) ) ;
d e l i v e r E v e n t ( t c ) ;end
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Rule examples : Prediction
r u l e "Predict NO3"// c a s c a d e d h y b r i d
when$s : Sample ( $ i d : i d )$n : Neura lNet ( $out : output == "no3" )
theni n s e r t (new Value ( $ id , $out , $n . e v a l ( $s ) ) ;
end
r u l e "Validate"// f u n c t i o n−embedding h y b r i d
when$s : Sample ( $ i d : i d )$v : Value ( i d == $id , t y p e == "no3" )e x i s t s SOM Neuron ( t h i s s i m i l a r $s )
theni n s e r t (new E s t i m a t e ( $s , $v ,
d r o o l s . getConsequenceDegree ( ) ) ;end
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Rule examples : Policy
r u l e "EoD"// time−aware r u l e w i t h f u z z y−>p r o b a b i l i t y mappingi m p l i c a t i o n @[ k ind=”fuz2prob ” , p r i o r = ” i d e n t i t y ” ]
when$ f : CurrPhase ( name == "anox" )
and @[ k ind=”Luk” ] // c o n f i g a t t r i b u t e s$m : TrendChange ( s i g n a l == "pH" , t y p e == "max" )
and$k : TrendChange ( s i g n a l == "orp" , t y p e == "knee_down" )
andTrendChange ( t h i s == $m, t h i s ov e r l a p s $k )
thenEndOfReact eod = new EndOfReact ( "denitro" ,
d r o o l s . getConsequenceDegree ( ) ;d e l i v e r E v e n t ( eod ) ;
end
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Rule examples : Policy II
r u l e "Switch"// i n j e c t i n g r u l e
when$ f : CurrPhase ( name == "anox" )$eod : EndOfReact ( r e a c t i o n == "denitro" )
thenSwitch sw = new Switch (+1); // n e x t phasei n j e c t (new Tuple ( sw ) , "holds" ) ;
i n s e r t ( sw ) ;end
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Hybrid Modules
Rule examples : Policy III
r u l e "Safe_Switch"// m u l t i−premise , g r a d u a l r u l e
when$s : Swi tch ( t h i s holds )and @[ kind=”prod” ]/∗ p ( S )∗ r (−S ) > p(−S )∗ r ( S ) ∗/imp l i e s (
Switch ( t h i s == @[ c r i s p ] $s ,t h i s neg holds and t h i s neg cost "falseN" )
Switch ( t h i s == @[ c r i s p ] $s ,t h i s holds and t h i s neg cost "falseP" )
)then
s c h e d u l e ( $s ,TMAX ∗ (1− d r o o l s . getConsequenceDegree ( ) ) ) ;
end
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
DROOLS
JBoss Drools
Business Rule Management System
Production Rules : RETE-based
Open Source
Modular
Expert : Object-Oriented Rule engineFlow : Support for Workflows
Fusion : Support for EventsGuvnor : Remote rule Repository
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈P(x),P(X )→C (Y )〉C (y)
Classic Modus Ponens
Premise and Implication entail Consequence
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈Φ(...,Aj(x)/εj ,... ),P(X )→C (Y )〉C (y)
Premise
Atomic constraints areevaluatedGeneral, pluggableEvaluatorsA Degree is returned
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈Φ(...,Aj(x)/εj ,... )/εP ,P(X )→C (Y )〉C (y)
Premise
Atomic constraints areevaluatedGeneral, pluggableEvaluatorsA Degree is returned
Premise
Atoms are aggregated informulasusing generalized logicConnectivesevaluated by Operators
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈P(x)/εP , →(X ,Y )/ε→〉C (y)
Implication
Implication has a Degreeoften given a priori
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈P(x)/εP , →(X ,Y )/ε→〉C (y)/εC
Implication
Implication has a Degreeoften given a priori
Modus Ponens
MP computes the Degreeof the Consequence
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Inference under Uncertainty
Generalized Inference
〈P1,→1〉C1/εC1
,...,〈Pn,→n〉Cn/εCn
C (y)/εC
Merging multiple sources
Multiple premises for the same conclusionSolve conflictsHandle missing values
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Rule Language and Engine
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Rule Language and Engine
Language extensions
Custom Evaluators
Before : limited support for boolean functions
After : integration with external modules
Adapter interfacesDegrees carry more information
Formulas
Before : conjunction, quantifiers, NaF
After : support for all standard connectives
Configuration Attributes
Before : parameters passed to custom evaluators only
After : granular configuration
Compile-time : choose implementationRun-time : configure propagation behaviour
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Rule Language and Engine
Language extensions : Example
r u l e "Rule"// custom : i m p l i c a t i o n s and MPi m p l i c a t i o n @[ degree =”0.75” ]d e d u c t i o n @[ kind=”min” ]
when$o1 : Type ( $ f 1 : f i e l d 1
/∗ custom : e x t e r n a l e v a l u a t o r ∗/== @[ id=”i1 ” , kind=”externa l ” , params=”...” ]
"val" )or @[ kind=”max” ] // custom : o p e r a t o r s
$o2 : AnotherType (f i e l d 3 == 0ˆˆ // custom : o p e r a t o r sf i e l d 3 == @[ c r i s p ] $ f 1 ) // custom : b e h a v i o u r
then/∗ consequence d e g r e e ∗/. . . = d r o o l s . getConsequenceDegree ( ) ;
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Rule Language and Engine
Engine Extension
Global additions
Evaluations and Degrees
Centralized Factory
Builds and converts degrees and operators
Improved RETE Network
Additional Nodes
Enabled NodeOperator Nodes
Including Implication and Modus Ponens
Augmented Alpha and Beta nodes
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Rule Language and Engine
Extended Engine : Example
#
thisenabled
#0
$o1
Type
#1
$f1
field1== val
#2
⊗1
#3
⊗3
#4
#
thisenabled
#5
$o2
AnotherType
#6
field3== 0
#7
#
6=2
#9
⊗1
#10
⊗3
#11
#
field3== $f1
#8
∨2#12
⊗1
#13
→0#14
⇒2#15 #Rule
1
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Outline
1 PhD Curriculum
2 IntroductionCase Study : Water TreatmentState of the Art
3 Hybrid ArchitecturesProposed ArchitectureHybrid Modules
4 Rule EngineInference under UncertaintyRule Language and Engine
5 Results and Future Works
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PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Results and Future Works
Results
Design and development of a configurable RETE engine
Added support for different non-boolean logics
Development of strongly coupled hybrid systems
Technology transfer : application of modern technologies toWWTPs
Future Developments
Release the engine as an official module (“Drools Chance”)
Integration of Rule-Based Systems and Ontologies
Application to different domains
-
PhD Curriculum Introduction Hybrid Architectures Rule Engine Results and Future Works
Results and Future Works
Results
Design and development of a configurable RETE engine
Added support for different non-boolean logics
Development of strongly coupled hybrid systems
Technology transfer : application of modern technologies toWWTPs
Future Developments
Release the engine as an official module (“Drools Chance”)
Integration of Rule-Based Systems and Ontologies
Application to different domains
PhD CurriculumIntroductionCase Study : Water TreatmentState of the Art
Hybrid ArchitecturesProposed ArchitectureHybrid Modules
Rule EngineInference under UncertaintyRule Language and Engine
Results and Future Works