jose maría alonso [email protected]
TRANSCRIPT
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Jose María [email protected]://www.softcomputing.es
Solving real-world problemsFrom computing with numbers to computing with words
From coal mining to intelligent data miningHuman- centric approaches
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European Centre for Soft Computing
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European Centre for Soft Computing
Private R&D CenterFour years of work(it was born in 2006)Located at Mieres (Asturias), Spain
SponsorsCajasturAsturian GovernmentSpanish Government (Mining funds)
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Activity (research and industry)
Basic and applied researchContribute to scientific advancementOrganizing workshops and conferences
ESTYLF 2008SCHSS 2009GEFS 2010SMPS 2010
Teaching activitiesSeminar programsInternational Summer Courses (2007, 2008, 2009)Master Course on Soft Computing and Intelligent Data Mining (2009-2010)
Technology transferImprove business competitivenessTechnology forum:
Automotive industry, Food industry, Renewable Energy, Digital TV, Logistics
DisseminationImproving the technological image of the regionApproaching science to society
Talks at high schoolsOne international Prize each year (M. Dorigo, P. Bonissone, W. Pedrycz)
IV Cajastur Mamdani prize for Soft Computing
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Staff
25 researchers (11 non-spanish)
15 people in management, administration and technical support positions
4 students from UniOvi
+ Affiliated researchers, Master students,
and Visitors
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Staff
RESEARCH
Principal ResearchersÓscar CordónEnric TrillasGracián TriviñoChristian BorgeltClaudio MoragaEnrique Ruspini
Asssistants and Postdocs(Young Researchers)
Sergio DamasGil GonzálezSergio GuadarramaJosé AlonsoLuka EciolazaArnaud QuirinWolfgang TrutschnigMarc SegondPrakash Shelokar
VisitorsJose SantamaríaMichio SugenoRudolf Seising
PredocsItziar García-HonradoAna Belén RamosÓscar IbáñezAlberto ÁlvarezSheila MéndezDavid PérezAlbert van der HeideKrzysztof TrawinskiRaiko SchulzSebastian Kaiser
UniOvi grantsCarmen CampomanesAdrian ÁlvarezLuis de ArquerAntonio Palacio
DirectionLuis MagdalenaRaul del CosoManuel Rodriguez
Management & adminNoelia BuenoCristina DiagoJosé Ramón GonzálezCarmen PeñaLaura RocesMaría Jesús SantanoÁlvaro VillagráCarmen Zarco
ICTBorja GómezMarcos MontoroDaniel ÁlvarezDaniel SánchezPablo SuárezDavid Rivera
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Research Units
Intelligent Data Analysis and Graphical ModelsChristian Borgelt (Germany)
Applications of Fuzzy Logic and Evolutionary AlgorithmsÓscar Cordón (Spain)
Cognitive computing: computing with perceptionsGracián Trivino (Spain)
Collaborative Soft Intelligent SystemsEnrique Ruspini (Argentina, USA)
Fundamentals of Soft ComputingClaudio Moraga (Chile)Enric Trillas (Spain)
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Scientific Committee
Chair: Lotfi Zadeh (USA) Vice-chair: Enric Trillas (ECSC)Secretary: María Ángeles Gil (Spain)
Members:Piero Bonissone (USA)Christer Carlsson (Finland)Janusz Kacprzyk (Poland)Rudolf Kruse (Germany)Xin Jao (UK)Javier Montero (Spain)Henri Prade (France)
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R&D Projects
18 ongoing research projects (european, national and regional) and contracts
7 Basic & applied research projects
14 Projects with companies3 CENIT projects and 3 Strategic projects
4 European projectsBISON projectCOST ActionMarie Curie Initial Training Network (MIBISOC)Marie Curie Fellowship for experienced researchers
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Master 2010 - 2011
Main featuresAcademic Period: September 13, 2010 to July 31, 2011Language: EnglishClass Schedule: From 15:30 until 20:00, Monday through FridayApplication Period
First pre-registration period: April 26th to May 15th Second pre-registration period: May 16th to July 10th
Course Fee: 2.300€ aproxScholarship: The Master offers scholarships that cover registration
fee, travel and living expenses. Scholarships will be awarded on thebasis of academic excellence
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What is Soft Computing?
Everytime you tell an outsider you work in Soft Computing or you apply Soft Computing techniques, the first question will be:
What is Soft Computing?
There is neither a single nor the best answer
We apologize for SC as a tool for solving real problems, so we need to explain SC to non-scientific people
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Revisiting several definitions
Soft computing has been defined from different points of view
PropertiesComparison (As opposite to …)PurposeComponents
“What is Soft Computing? Revisiting possible answers”Luis Magdalena Plenary lecture at FLINS’08
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Soft Computing by properties
Every computing process that purposely includes imprecision into the calculation on one or more levels and allows this imprecision either to change (decrease) the granularity of the problem, or to "soften" the goal of optimization at some stage, is defined as to belonging to the field of soft computing
X. Li, D. Ruan, and A.J. van der WalDiscussion on soft computing at FLINS’96
Int. Journal of Intelligent Systems, Vol. 13, (2–3), pp. 287–300 (1998)
ApproximateReasoning
FunctionalApproximation
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Soft computing as opposite to …
Symbolic LogicReasoning
Classical Numerical and
Search Methods
ApproximateReasoning
FunctionalApproximation/Random search
HARD COMPUTING SOFT COMPUTING
Precise/Explicit Models
Approximate/Implicit Models
Piero P. BonissoneSoft Computing: the convergence of emerging computing technologiesSoft Computing, Vol 1 (1), pp 6-18 (1997)
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Soft computing as opposite to …
Actually, the distinguishing feature of soft computing is straightforward. Hard computing uses an explicit model of the process under consideration while Soft computing does not do this. Instead, as an indispensable preliminary step, it infers an implicit model from the problem specification and the available data.
Stephen W. KercelGuest Editorial, Special Issue: Industrial Applications of Soft Computing IEEE Trans. on Systems, Man and Cybernetics-Part C, Vol. 36 (4), pp 450-452 (2006)
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Soft computing by purpose
The guiding principle of soft computing is:
Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness, low solution cost and better rapport with reality.
L.A. ZadehSoft computing and fuzzy logicIEEE Software, Vol 11 (6), pp 48–56 (1994)
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Soft computing by components
… soft computing is a partnership of distinct methods …The principal constituents of soft computing are fuzzy logic, neurocomputing, and probabilisticreasoning, with the latter subsuming genetic algorithms, belief networks, chaotic systems, and parts of learning theory.
L.A. ZadehSoft computing and fuzzy logicIEEE Software, Vol 11 (6), pp 48–56 (1994) Approximate
ReasoningFunctional
Approximation/Random search
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Soft Computing in practice
Solvingreal-worldproblems
Approximate reasoningExpert KnowledgeIntelligent Data AnalysisAutomatic LearningDecision MakingHandling Uncertainty
Networks
NeuralNetworks
EvolutionaryComputation Probabilistic
Reasoning
FuzzyLogic
FuzzyEvolutionaryAlgorithms
GeneticFuzzy
Systems
NeuroFuzzySystems
FuzzyNeural
Networks
GeneticNeural
Genetic
Networks
BayesianNetworks
NeuralNetworks
EvolutionaryComputation Probabilistic
Reasoning
FuzzyNeural
Networks
FuzzyLogic
NeuroFuzzySystems
NetworksNeuralGenetic
GeneticFuzzy
AlgorithmsEvolutionary
Fuzzy Systems
GeneticBayesian
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Soft Computing techniques
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Fuzzy Logic
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Fuzzy Logic
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Fuzzy Logic
OLD
0 10025 50 75 906515 35
YOUNG MIDDLE−AGE
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Fuzzy Logic
YOUNG
0 10025 50 75 906515 35
MIDDLE−AGE OLD
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Fuzzy Logic
YOUNG
0 10025 50 75 906515 35
OLDMIDDLE−AGE
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Fuzzy Inference Systems (FIS)
The most commonly used fuzzy inference technique is the so-called Mamdanimethod (1975): Min-Max
Fuzzification of inputsRule evaluation (conjunction / implication): MinimumAggregation of rule outputs: MaximumDefuzzification
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Fuzzy Inference Systems (FIS)
A simple example: two-input one-output problem that includes three rulesNOTE: this example is taken from “Artificial Intelligence. A guide to Intelligent Systems” (Michael Negnevitsky, Addison Wesley)
Rule: 1Rule: 1 Rule: 1Rule: 1IFIF x x is is AA3 3 IF IF project_funding project_funding is is adequate adequate OR OR y y is is BB1 1 OR OR project_staffing project_staffing is is small small THEN THEN z z is is CC1 1 THENTHEN risk risk is is lowlow
Rule: 2Rule: 2 Rule: 2Rule: 2IF IF x x is is AA2 2 IF IF project_funding project_funding is is marginal marginal AND AND y y is is BB2 2 AND AND project_staffing project_staffing is is large large THEN THEN z z is is CC2 2 THEN THEN risk risk is is normalnormal
Rule: 3Rule: 3 Rule: 3Rule: 3IFIF x x is is AA1 IF 1 IF project_funding project_funding is is inadequateinadequateTHEN THEN z z is is CC3 3 THENTHEN risk risk is is highhigh
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FIS: Fuzzification
Crisp Input
0.1
0.71
0 y1
B1 B2
Y
Crisp Input
0.20.5
1
0
A1 A2 A3
x1
x1 Xμ(x = A1) = 0.5μ(x = A2) = 0.2
μ(y = B1) = 0.1μ(y = B2) = 0.7
NOTE: this example is taken from “Artificial Intelligence. A guide to Intelligent Systems” (Michael Negnevitsky, Addison Wesley)
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FIS: Rule evaluation
A31
0 X
1
y10 Y0.0
x1 0
0.1C1
1
C2
Z
1
0 X
0.2
0
0.2 C11
C2
Z
A2
x1
Rule 3: IF x is A1 (0.5)
A11
0 X 0
1
Zx1
THEN
C1 C2
1
y1
B2
0 Y
0.7
B10.1
C3
C3
C30.5 0.5
OR(max)
AND(min)
OR THENRule 1: IF x is A3 (0.0)
AND THENRule 2: IF x is A2 (0.2)
y is B1 (0.1) z is C1 (0.1)
y is B2 (0.7) z is C2 (0.2)
z is C3 (0.5)NOTE: this example is taken from “Artificial Intelligence. A guide to Intelligent Systems” (Michael Negnevitsky, Addison Wesley)
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FIS: Aggregation
00.1
1C1
z is C1 (0.1)
C2
00.2
1
z is C2 (0.2)
0
0.5
1
z is C3 (0.5)
ZZZ
0.2
Z0
∑
C30.50.1
NOTE: this example is taken from “Artificial Intelligence. A guide to Intelligent Systems” (Michael Negnevitsky, Addison Wesley)
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FIS: Defuzzification
Centroid Center of Gravity (COG)
NOTE: this example is taken from “Artificial Intelligence. A guide to Intelligent Systems” (Michael Negnevitsky, Addison Wesley)
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Solving real-world problems
Three illustrative examplesWIFIROBOTS
A team of robots is in charge of extending a wireless networkSARBIA
Advanced irrigation system based on Soft Computing techniquesLILA-CATA
Assessing the quality of asturian cheeses
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Ambient Intelligence
Cognitiveactuators
SOFT
COMPUTING
Cognitiveactuators
SOFT
COMPUTING
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Ambient Intelligence
1999: The IST Advisory Group of the UE introduces the term AmbientIntelligence (AmI) as part of the preparations for the FP62000: The first document describing future applications for AmI
(absolutely wrong prediction)Scenarios for Ambient Intelligence in 2010(http://cordis.europa.eu/fp7/ict/istag/home_en.html)
Road WarriorDimitrios and the Digital-MeCarmen – traffic, sustainability, and commerceAnnette and Solomon in the ambient for social learning
BasicsUbiquitous computingPervasive computingContext awarenessProfiling practicesHuman-centric computer interaction
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WIFIROBOTS
Dynamic extension of a WiFi network
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WIFIROBOTS
AMIGOBOTS (MobileRobots Inc.)
Weight: 3.6kg / Carga: 1kgBatery: 3h8 ultrasounds (12cm / 5m)44 MHz Renesas SH2-7144RS-232, WiFi, Ethernet
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WIFIROBOTS
AP Thomson
AP HULK
AP ROBOTINRouter MASTER
AP ECSC
AP ECSC
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WIFIROBOTS
Soft ComputingThe WiFi signal is extremely noisy (multi-path effect)
Handling uncertainty by means of Fuzzy LogicIndoor localization system based on Fuzzy Logic
Enhancing navigationFuzzy Finite State Machines, Aggregation Operators, etcFusion of sesors: WiFi + ultrasounds + odometry
Collaborative tasksFuzzy Logic + Multi-objective Evolutionary AlgorithmsSLAM (Self-localization and mapping)
Combining partial observations of the whole environment
Real-time robot deploymentCommunication, planning, routing, etc.
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WIFIROBOTS
WiFi localization by meansof Triangulation
Signal strength strongly depends on distance and obstaclesWiFi frequency (2.4GHz): water resonancy (human interference)WiFi channel is very noisy
Reflection + Refraction + Diffraction => Multipath effect
PROPAGATION MODEL
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WIFIROBOTS
WiFi localization by means ofFuzzy Rule-based Systems
IF Signal received from APi is High AND
Signal received from APj is Low THEN
The robot is close to Position k
0.78
0.0
0.5
1.0
UuUl
Low Medium High
xi
0.22
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SARBIA
RequirementsEfficiency / SecurityRemote controlAutomatic data acquisitionData analysisFusion of heterogeneous informationLearning capabilitiesAutonomous decision makingHigh interaction with humansAdding expert knowledgeExplaining decisions in a comprehensible wayIncorpotating feedback informationClose Loop: sensors + reasoning + actuators
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SARBIA
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LILA-CATA
Cheese ripening process (cure)
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LILA-CATA
Quality evaluationPhysico-quimical analysis (It can be eaten - Healthy)Sensor analysis (It can be sold as certified)
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Assessing the quality of asturiancheeses by means of combiningstatistics and Soft Computing
LILA-CATA
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LILA-CATA
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LILA-CATA
Assessing the quality of asturianchesses by means of combiningstatistics and Soft Computing
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Conclusions
Soft Computing represents a computational approach to solve problems under circumstances of uncertainty and/or imprecision, either inherent or “added”Uncertainty or imprecision is not a target, it is a fact or a meanCore techniques are Fuzzy Logic, Neural Networks, Evolutionary Computation and Probabilistic ReasoningOther components are granular computing, bio-inspired search and optimization, or computing with wordsHybridization is one of the central aspects of the fieldIs particularly focused on real-world problems, working on the basis of approximate and implicit models achieving good (but notoptimal) solutionsApparently there are some differences in between Soft Computing and Computational Intelligence (not very significant in practice)
CI - Biologically and linguistically motivated computational paradigmsSC - Computing processes that purposely include imprecision (decreasing) the granularity or "softening" the goal
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What is CI?
Computational intelligence (CI) is a recently emerging area of fundamental and applied research exploiting a number of advanced information processing technologies. The main components of CI encompass neural networks,fuzzy set technology and evolutionary computation.
Witold PedryczComputational Intelligence: An IntroductionCRC Press, 1998
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What is CI?
The Field of Interest of the Society shall be the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained.
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What is CI?
The Field of Interest of the Society shall be the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained.
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