Intelligent Decision Support Systems 2-MAI-1112.pdf · Intelligent Decision Support Systems Intelligent Decision Support Systems (Part II - EVOLUTION OF DECISION SUPPORT SYSTEMS

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Intelligent Decision Support Systems

(Part II - EVOLUTION OF DECISION SUPPORT SYSTEMS / INTELLIGENT DECISION SUPPORT SYSTEMS / AN EXAMPLE)

Miquel Sànchez i Marrè, Karina Gibert

[email protected], [email protected]

http://kemlg.upc.edu/menu1/miquel-sanchez-i-marre

http://kemlg.upc.edu/menu1/karina-gibert

Course 2011/2012


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PART 2 – EVOLUTION OF DECISION SUPPORT SYSTEMS / INTELLIGENT DECISION SUPPORT SYSTEMS / AN

EXAMPLE


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EVOLUTION OF DECISION SUPPORT SYSTEMS

Historical Perspective of Management Information Systems

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© Miquel Sànchez i Marrè & Karina Gibert, KEMLG, 20114

Management Information Systems (1) Transaction Processing Systems

early 70s Electronic data processing Large volumes of commercial transactions Batch processing Non interactive

Decision Support Systems (DSS) Started in Middle 70s Interactive systems Supporting business decision making Early DSS

Late 70s Very rudimentary (spredadsheet software)

Advanced DSS 80s Using Operations Research and Management Science Models “what if” analysis

Intelligent Decision Support Systems (IDSS) [90s]

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Management Information Systems (2)

MANAGEMENT INFORMATION SYSTEMS

TRANSACTION PROCESSING

SYSTEMS

DECISION SUPPORT SYSTEMS

(DSS)

MODEL-DRIVENDSS

DATA-DRIVENDSS

AUTOMATICMODEL-DRIVEN

DSS

TOP-DOWNAPPROACH /

CONFIRMATIVE MODELS

BOTTOM-UPAPPROACH /

EXPLORATIVE MODELS

STATISTICAL / NUMERICAL

METHODS

ARTIF. INTELLIG / MACH. LEARN.

METHODS

INTELLIGENT DECISION SUPPORT SYSTEMS IDSS)


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Decision Support Systems

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Need for Decision Making Support Tools

Complexity of the decision making process

Accurate Evaluation of multiple alternatives

Need for forecasting capabilities

Uncertainty

Data Analysis and Exploitation

Need for including experience and expertise (knowledge)

Computational tools: Decision Support Systems (DSS)

Intelligent Computational Tools: Intelligent Decision Support Systems (IDSS)

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Decision Support SystemsDefinition

A Decision Support System (DSS) is a system under the control of one or more decision makers that assists in the activity of decision making by providing an organised set of tools intended to impart structure to portions of the decision-making situation and to improve the ultimate effectiveness of the decision outcome [Marakas, 1999].

A Decision Support System (DSS) is a system which lets one or more people to make decision/s in a concrete domain, in order to manage it the best way, selecting at each time the best alternative among a set of alternatives, which generally are contradictory

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Decision Support Systems

DSS in Business and Management Decisions on prices Decisions on products Decisions on marketing Decisions on employees Decisions on strategic planning

DSS in Engineering Decisions on product design Decisions on process supervision / control

DSS in Financial Entities Decisions on loans

DSS in Medicine Decisions about presence/absence of illness Decisions on medical treatments Decisions on specialised medical machine control

DSS in Environmental Systems (EDSS) Decision about reliable and safe environmental system management


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Advanced Decision Support Systems

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Advanced Decision Support Systems (1)

DSS are a model-based set of procedures for processing data and judgments to assist a manager in his/her decision [Little, 1970]

DSS couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It’s a computer-based support for management decision makers who deal with semi-structured problems[Keen & Scott-Morton, 1978]

DSS is a system that is extendable, capable of supporting ad hoc analysis and decision modelling, oriented towards future planning, and of being used at irregular, unplanned intervals [Moore & Chang, 1980]

Definitions

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DSS enable managers to use data and models related to an entity (object) of interest to solve semi-structured and unstructured problems with which they are faced [Beulens & Van Nunen, 1988]

Main feature of DSS rely in the model component. Formal quantitative models such as statistical, simulation, logic and optimisation models are used to represent the decision model, and their solutions are alternative solutions [Emery, 1987; Bell, 1992]

DSS are systems for extracting, summarising and displaying data [McNurlin & Sprague, 1993]

Definitions

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Data Analysis

Monitoring / Control

Prediction

Planning

Management

Data Recording / Retrieval

Tasks

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Statistical /Numerical Models Linear Models, Logistic Regressions Clustering Data Analysis (PCA, DA, SCA, MCA...) Markovian Models

Simulation Models

Control Algorithms

Optimization Techniques Linear Programming Queue Models Inventory Models Transport Models Multiple Criteria Decision Making (MCDM)

Models

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Advanced Decision Support Systems (5)From observations to decisions [adapted from A.D. Witakker, 1993]

15

Observations

Consequences

Recomendations

Predictions

KNOWLEDGE

Understanding

Data

Quantiy of Information

DECISION

INTERPRETATION

High Low

High

Low

Value andRelevance

toDecisions

Observations

Consequences

Recomendations

Predictions

KNOWLEDGE

Understanding

Data

Quantiy of Information

DECISION

INTERPRETATION

High Low

High

Low

Value andRelevance

toDecisions

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Advanced Decision Support Systems (6)Intelligence Density [V. Dhar & R. Stein, 1997]

Intelligence Density : the amount of useful decision support information that a decision maker gets from using the output from some analytic system (IDSS) for a certain amount of time

How much of the IDSS output do you have to examine before you can make a decision of a specified quality ?

How quickly can you get the essence (knowledge) of the underlying data from the IDSS output ?

Conceptually, Intelligence Density can be viewed as the ratio of the number of utiles (utility units) of decision-making power gleaned (quality) to the number of units of analytic time spent by the decision maker.



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INTELLIGENT DECISION SUPPORT SYSTEMS

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Intelligent Decision Support Systems (1)

An IEDSS is an intelligent information system that reduces the time in which decisions are made in an environmental domain, and improves the consistency and quality of those decisions [Haagsma & Johanns, 1994]

A DSS is a computer system that assists decision makers in choosing between alternative beliefs or actions by applying knowledge about the decision domain to arrive at recommendations for the various options. It incorporates an explicit decision procedure based on a set of theoretical principles that justify the “rationality” of this procedure [Fox & Das, 2000]

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Intelligent Decision Support Systems (2)

The use of Artificial Intelligence tools and models provides direct access to expertise, and their flexibility makes them capable of supporting learning and decision making processes. There integration with numerical and/or statistical models in a single system provides higher accuracy, reliability and utility [Cortés et al., 2000]

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Intelligent Decision Support Systems (IDSS) Typology

IDSS in static domains Industrial process design systems

IDSS in dynamic domains Wastewater Treatment Plant Supervisory System

IDSS in dynamic domains with real-time constraints or possible catastrophic consequences Automatic Supervisory System in a medical Intensive

Cure Unit Environmental Emergency management systems

On-line: Emergency coordination systems Off-line: Training systems for emergency management

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Conceptual Components of an IDSS

ARTIFICIAL INTELLIGENCETECHNIQUES

STATISTICAL / NUMERICALMETHODS

GEOGRAPHICAL INFORMATION SYSTEMS /COMPLEMENTARY SYSTEMS

ENVIRONMENTAL / HEALTH /MANAGEMENT / BUSINESS

ONTOLOGIES

TEMPORALREASONING

UNCERTAINTY MANAGEMENT

SPATIALREASONING

ECONOMICALCOSTS

VALIDATION OF IDSS

INTELLIGENT DECISION SUPPORT SYSTEMS


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IDSS Architecture

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IntelligentDecisionSupportSystems(IDSS) DATA BASE

(TEMPORAL DATA)

SYSTEM / PROCESS

SENSORS

SUBJECTIVEINFORMATION /

ANALYSES

Spatial /Geographical

data

On-linedata

Off-linedata

EXPLANATION ALTERNATIVES EVAL.

PLANNING / PREDICTION / SUPERVISION

REASONING / MODELS’ INTEGRATION

DATA MININGKNOWLEDGE ACQUISITION / LEARNING

AIMODELS

STATISTICALMODELS

NUMERICALMODELS

USER INTERFACE

USER

ON-LINE / OFF-LINE

ACTUATORS

ECONOMICALCOSTS

LAWS &REGULATIONS

Feedback

DAT

A I

NTE

RPRE

TATI

ON

D

ATA

MI

NIN

G

DIA

GN

OSI

S

D

ECIS

ION

SU

PPO

RT

Background/SubjectiveKnowledge

Decision /Strategy

GIS(SPATIAL DATA)

Actuation


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IDSS Analysis and DesignRequirements, advantages and limitations

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IDSS Requirements

Type of decision problem (policy, operations, resource allocation, etc.)

Domain and scope of the decision problem

Data and knowledge availability

Organizational and structural boundaries

Decision-making process

Impact on and synergy with the existing sytems

Expected consequences of decision execution

Profiles of decision-makers (users of the system)

External constraints and contexts

Objectives of the IDSS

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Kind of Support provided by an IDSS[Marakas, 1999]

Identify the need for decisions

Identify specific problems that require attention

Solve or assist in solving problems

Help compensate for cognitive limitations of human decision makers

Provide assistance in the form of advice, analysis, or evaluation

Enhance user creativity, imagination, or insight

Facilitate interactions in multiparticipant decision maker settings

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IDSS Requirements and AnalysisThe Stretch Plot: Dimensions for Requirements and Quality Solution[V. Dhar & R. Stein, 1997]


Accuracy

Explainability

Response Speed

Scalability

Compactness

Flexibility

Easy of Use Embeddability

Tolerance for Complexity

Tolerance for Noisein Data

Tolerance forSparse Data

Learning Curve

DevelopmentSpeed

ComputingEase

Independence from Experts

Model Related

Con

stra

intR

elat

ed

Organization Related

Qua

lity

Rel

ated

QUALITY OF THE SYSTEM ENGINEERING OF THE SYSTEM

QUALITY OF AVAILABLE RESOURCES

LOGISTICAL CONSTRAINTS

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Design and

Building of an IDSS


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IDSS Validation

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IDSS Validation Criteria (1)Adapted from [Klein & Methlie, 1995]

System Performance Efficiency and response time Data entry Output format Hardware Usage Man-machine interface

Task performance Decision-making time, alternatives, analysis, quality and

participants User perceptions of trust, satisfaction and understanding

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IDSS Validation Criteria (2)Adapted from [Klein & Methlie, 1995]

Business Opportunities Costs of development, operation, and maintenance Benefits associated with increased income and reduced

costs Value to the organization of better service, competitive

advantage, and training

Evolutionary Aspects Degree of flexibility, ability to change Overall functionality of the development tool


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Implementation of an IDSS in a computer. Anexample: Intelligent Environmental System Management

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IntelligentEnvironmental

Decision Support Systems (IEDSS)

DATA BASE(TEMPORAL DATA)

ENVIRONMENTAL SYSTEM / PROCESS

SENSORS

BIOLOGICAL /CHEMICAL /PHYSICALANALYSES

Spatial /Geographical

data

On-linedata

Off-linedata

EXPLANATION ALTERNATIVES EVAL.

PLANNING / PREDICTION / SUPERVISION

REASONING / MODELS’ INTEGRATION

DATA MININGKNOWLEDGE ACQUISITION / LEARNING

AIMODELS

STATISTICALMODELS

NUMERICALMODELS

USER INTERFACE

USER

ON-LINE / OFF-LINE

ACTUATORS

ECONOMICALCOSTS

ENVIRONMENTAL /HEALTH

REGULATIONS

Feedback

DAT

A I

NTE

RPRE

TATI

ON

D

ATA

MI

NIN

G

D

IAG

NO

SIS

D

ECIS

ION

SU

PPO

RT

Background/SubjectiveKnowledge

Decision /Strategy

GIS(SPATIAL DATA)

Actuation

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Intelligent Management of Environmental Systems

Evolutionary Environmental Data Base Management

Intelligent Data Analysis

Acquisition, Learning and Knowledge Management

Reasoning

Cooperation and Integration of several techniques

Decision Making Support

Actuation over the system

Learning

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Operation in a WWTP!

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Proposal for WWTP supervision

HISTORY & ALARMS

LABORATORY

IN-SITU & MICROSCOPICOBSERVATIONS

Operators

MANUALCONTROL

AUTOMATIC CONTROLON DO, RECIRCULATION FLOW,

WASTE FLOW & BY-PASS

Sist. Infom.

ANALITICAL

Laboratory

ON-LINEDATA

Sensors

PLANTMANAGER

ES

SISTEMASUPERVISOR

ACTUATIONPLAN PREDICTION

CBR GPS-X/XT

SUPERVISION

DSS Computer

PLC


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Evolutionary Environmental Data Base Management

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DATA VALIDATION: noise, outliers and missing values

DATA INTEGRATION: homogeneous units and time scale

ON-LINE DATA: 210 digital & 20 analogical signalse.g. pH, flow rates (air, water and sludge), equipment status (valves,pumps, etc.), dissolved oxygen...

OFF-LINE DATA: 158 variablesQUANTITATIVE: e.g. COD, BOD, (V)SS, N, P, Temperature, metals,grease & oils, V30... at different sampling location

QUALITATIVE: e.g. floc characterisation, filamentous bacteria,protozoa and metazoa identification and presence, presence ofbubbles, settling observation...

Data Monitoring

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HSS First level: data gathering


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Intelligent Data Analysis

Acquisition, Learning and Knowledge Management

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Knowledge Acquisition?

Knowledge is the crux matter in natural resources and environmental process management

Decision Making Support must be based on the knowledge

Knowledge acquisition and management from huge amounts of real must be accomplished

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Intelligent Data

Analysis• TRANSITION NETWORKS• TEMPORAL CBR• DYNAMIC CANDID• TIME SERIES

STATISTICAL DATA PROCESSING

AGENT

ENVIRONMENTALDATABASE

FILTERED DATA

CLUSTERINGTECNHIQUES

AGENT

CASE-BASEDREASONING

TECNHIQUESAGENT

REINFORCEMENT /SUPPORT VECTOR

MACHINETECNHIQUES

AGENT

DYNAMICAL ANALYSIS

TECNHIQUESAGENT

EXPERT / USERENVIRONMENTAL

SCIENTIST

GOALSDEFINITION

KNOWLEDGEMANAGEMENT

• Statisticalanalysis

• NN• COBWEB/3• ISODATA• K-MEANS• MARATA

• OPENCASE • CANDID

RECOMMENDER /META-KNOWLEDGE

AGENT

• On-line help• Suggestion of methods

UNIFICATION/INTEGRATION of METHODS/PATTERNS

VALIDATION/INTERPRETATION/DOCUMENTATION

USE ofPATTERNS/KNOWLEDGE

• Prediction• Diagnosis• Planning• Decision support

EXPERT / USERENVIRONMENTAL

SCIENTIST

KNOWLEDGE

RE

CO

MM

EN

DA

TIO

N A

ND

ME

TA

-KN

OW

LK

ED

GE

MA

NA

GE

ME

NT

DA

TA

FILT

ER

ING

KN

OW

LE

DG

E D

ISCO

VE

RY

KN

OW

LE

DG

E M

AN

AG

EM

EN

T

DECISIONTREE

TECNHIQUESAGENT

RULEINDUCTION

TECNHIQUESAGENT

• ID3• CART• C4.5

• CN2• PRISM• RULES• RISE

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STEPS OF THE METHODOLOGY

AbstractionATTRIBUTE

ANDOBSERVATION

SELECTION

CLASSIFICATION

GENERATIONOF

RULESKNOWLEDGE

BASE

Observations:Attribute/Valuesmatrix

Newattributes/observations

Classes Newclassificationparameters


DOMAIN

ATTRIBUTERELEVANCE

MODULE

VALIDATION

E X

P

E

R

TKnowledgePatterns (1): Clustering withLINNEO+

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Knowledge Patterns (2)Q-SP3

SS-AT MICO

COD-SP3 FILAM-PREDOM

C2

<= 929

<= 14

> 0 <= 0

ROTIFER

CIL-CAR T41

<= 1

AMEB COD-AB

<= 1 > 1

COD-SP1 ROTIFER

<= 1

COND-ATC1

> 256 <= 256

C1 C5

<= 898> 898

C1 C19

<= 577> 577> 1

C7 C5

<= 0 > 0

> 1

> 929

> 14

C15 NFILAM

> 0 <= 0

BIODIV-MIC

C8

> 1 <= 1

C19 C1

<= 5 > 5

C5

C1

noc, noc_41,zoo_noc, noc_thio,41, zoo_mico,zoo_41

C16

zoo mico

C9

thio

ESC-B AMEB

<= 1

> 3 <= 3

Q-SP1

COD-EMIS NFILAM

ZOO ASP-AT<= 90 > 90

GPROTO-PREDOM

> 1<= 2

> 1

C8 C1

Sessils,vort_gflag

<=6207

> 1

C14

> 2 <= 2

<= 1

C1121N/THIO

C2 C17

<= 2 > 0

C20

>6207

OPER NOC

C2 AMEB

> 1

C2 C11

<=0 >0

Q-ABC2

> 2

> 1 <= 1

C2 C11

<=11068 >11068

C11<= 1

C4 C3 C2

sessils_rotifer

cap

pflagcrawling,sessils_crawling

T41

> 0 <= 0

OPER

C7

> 1 <= 1

C10

<= 1 >1

C2

(2.0/1.8)

(3.4/2.1) (4.5/1.8)

(66.9/7.2)

(2.0/1.8) (4.2/1.3)

(3.0/1.1)(5.0/2.3)

(3.0/1.1)

(8.0/2.4)(2.0/1.0)

(1.0/1.8) (2.0/1.0)(4.0/2.2)

(5.0/2.3)

C5

cap

(0.0)

(2.0/1.0)

Ameb, sessils_ameb, cilcar,amebteca_rotifers, crawling_cilcar,gflag, crawling_gflag, amebteca,vort_cilcar, vort, cilcar_rotifer,

C4

(0.0) (2.0/1.0) (2.1/1.1) (0.2/0.2) (1.0/0.8) (3.0/2.8)

(2.0/1.8) (4.0/2.2)

(2.0/1.0)

(38.0/2.6)

(7.6/3.5) (2.0/1.0)

(6.0/2.3)

(2.0/1.0) (17.0/1.3)

(3.1/2.1) (9.0/2.4)

(2.0/1.8)

(2.0/1.8)(18.0/2.5)

(18.0/2.5)

Decision Tree Induction

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Knowledge Patterns (3)IF Q-AB < 9083.00AND Q-SP3 < 2227.00AND COND-AT > 847.00AND CIL-CAR < 0.50AND AMEB < 0.50THEN WWTP-problems = c1(47/81)

IF TERB-AB > 123.50AND Q-SP1 > 6223.50AND Q-SP3 > 3467.50AND 33.00 < DQO-AT < 82.00AND COND-AT < 1330.00AND NOC > 0.50AND P-FLAG < 3.50THEN WWTP-problems = c2(38/55)

IF Q-AB < 10924.50AND Q-SP3 > 1030.00AND NOC < 1.50AND MICO > 1.50THEN WWTP-problems = c10(17/17).

IF PH-AB < 7.40AND FILAM-DOMI = thioTHEN WWTP-problems = c9(3/3)

Inference Rule Induction

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Knowledge Patterns (4)...............................................................................................................................................................................

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GPROTO-DOMI

FILAM

BIODIV.MIC

COND-AB

Q-AB

NO3-AT

NH4-AT

NFILAM

COD-SP1

Q-SP3

ESC-B

COD-AT

IVF

COD-AB

CM

N

H

?

H

N

Cap

H

L

N

N

?

Cap

Case 100

LH

N

Case 106

N

Case 108

H

N

N

N

Noc

H

L

N

Case 101

N

Case 102

H

N

?

H

?

H

N

N

?

?

Cap

Case 163

N

Case 158

H

Case 155

VH

N

N

Mico

N

N

N

H

Case 157

N

N

Mico

N

?

Cap

N

Case 160H

Case 161

L

L

H

Cap

Case 143

H

Case 153P-flag

Case 144

Amebtecarotifer

N

N

N

N

Thio

L

?

Case 47

N?

Case 46Case 43

Sessils/crawling Crawling

N

H

N

Case 65

H

Noc

Case 178

HN

N

N

N

N

?

N

Case 92Case 179

P-flagVort_gflag

Case 91

Sessils

N

N

Case 172

Zoo/Mico

?

N

N

N

L

Mico

Case 154Case 162

N

H

N

N

H

Cap

Case 107

N

Case 175

?

N

N

Thio

N

L

?

?

Cap

Cap

N

HCase 48

Case 38

H

Case 193

Case 172

Case 159

Case 40

Zoo/41Mico

ZooThio

Case 51

Noc/Thio

LN

......

(L) Low(N) Normal(H) High

Discriminant Tree Induction


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Reasoning

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Expert Knowledge

Prototypical situations

Experts’ domain knowledge

Knowledge acquisition Building-up decision trees Expert interactive knowledge explicitation Machine learning methods

Supervised methods Unsupervised methods

LINNEO+

Inference rules: rule-based reasoning

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STEPS OF THE METHODOLOGY

AbstractionATTRIBUTE

ANDOBSERVATION

SELECTION

CLASSIFICATION

GENERATIONOF

RULESKNOWLEDGE

BASE

Observations:Attribute/Valuesmatrix


Classes Newclassificationparameters


DOMAIN

ATTRIBUTERELEVANCE

MODULE

VALIDATIONE

X

P E

R

TExpert KnowledgeAcquisition: LINNEO+

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SCREEN-KBS-RULE-008 ::IF I-COD 150 mg/L AND I-COD 400 mg/LTHEN conclude that

I-COD-status is normal

BIOLOGICAL-REACTORS-KBS-RULE-023 ::IF DO 0.5 mg/L AND DO 2.5 mg/LTHEN conclude that

DO-level is normal

BIOLOGICAL-REACTORS-KBS-RULE-025 ::IF B-VSS > 2500 mg/LTHEN conclude that

B-VSS-status is high

SECONDARY-SETTLER-KBS-RULE-031 ::IF O-COD 75 mg/LTHEN conclude that

O-COD-status is high

SUPERVISORY-KBS-RULE-002 ::IF I-COD-status is normal AND O-COD-status is highTHEN conclude that

COD Removal efficiency is low

SUPERVISORY-KBS-RULE-037 ::IF COD Removal efficiency is low AND B-VSS-status is highTHEN conclude that

SIDESTREAMS situation is VERY-POSSIBLE

LOCAL-DIAGNOSIS RULES

COMBINATION RULES

Diagnosis Rules

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SHELLMecanismed’inferència

USUARI

Interfíciegràfics/menus

BASE DE CONEIXEMENTReglesÀrbres de decisió

DADES EXTERNESSensorsLaboratoriObservacions

Aplicacionsexternes

SBC

Knowledge-Based System

Architecture of a Knowledge-Based System

Knowledge Acquisition

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Experiential Knowledge

Ideosyncratic situations

Practical experiented knowledge

Cases or experiences: case-based reasoning

Learning

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Case-Based Reasoning

CASELIBRARY

INDEX

NEWCASE

SUCCESSFUL/FAILURE

SOLUTION

BEST CASE

ADAPTEDSOLUTIONDOMAIN

KNOWLEDGE

RETRIEVAL

RETRIEVEDCASES

SIMILARITY ASSESMENT

EVALUATION

ADAPTATION

LEARNING

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Case Structure

( :identifier CASE-31:situation-description ( (Water-inflow 22,564 m3/day)

(Inflow-Biological-Oxygen-Demand 192.9 mg/L). . . )

:diagnostics STORM-SITUATION:actuation-plan( (1 Close Purge-flow)

(2 Maintain-DO-predictive-control). . . )

:case-derivation CASE-11:solution-result SUCCESS:utility-measure 0.63:distance-to-case 0.1305 )

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Atribut Ordre Pes RangBaix (B) Normal (N) Alt (A) Molt Alt (MA)

DQO-Aigua Tractada (DQO-AT)Index Volumètric Fangs (IVF)Presència Escumes Aireació (ESC-B)DQO-entrada (DQO-AB)Biodiversitat microfauna (BIODIV-MIC)Conductivitat-entrada (COND-AB)Cabal-entrada (Q-AB)DQO-Sortida Primari#1 (DQO-SP1)Cabal-Sortida Primari#3 (Q-SP3)Nitrat-Aigua Tractada (NO3-AT)Amoni-Aigua Tractada (NH4-AT)Número Filamentosos (NFILAM)Càrrega màsssica (CM)SST-Aigua Tractada (SST-AT)Aspecte-Aigua Tractada (ASP-AT)

1234

6789

1011121415--

8.0109.05.0

8.05.06.05.01.077888

10

( 0 - 40 )( 0 - 275 )

-( 0 - 400 )

( 0 – 2.1 )( 0 - 900 )( 0 - 4500 )( 0 - 250 )( 0 - 1000 )

( 0 - 1 ) ( 0 - 8.0 )

-( 0 - 0.25 )( 0 – 10)( 0 – 1 )

( 40 - 70 )( 275 – 500 )

( 0 – 2.5 )( 400 – 950 )

( 2.1 – 4.5 )( 900 – 1800 )

( 4500 – 17000 )( 250 – 550 )

( 1000 – 5500 )( 1 - 6 )

( 8.0 - 25 )( 0 – 4.25)

( 0.25 - 0.35 )( 10 – 20 )( 1 – 2 )

( 70 – 150 )( 500 – 1100 )

( 2.5 – 5)( 950 – 1500 )

( 4.5 – 8 )( 1800 – 2500 )(17000–30000)( 550 –1500 )

( 5500 – 11000 )( 6 – 20 )( 25 - 50 )

( 4.25 – 8 )( 0.35 - 1.5 )( 20 – 50 )( 2 – 5 )

( 150 – 500 )--

( 1500 – 3000 )

-(2500-10000)

(30000-50000)-

(11000-30000)( 20 – 60 )( 50 –100 )

-( 1.5 – 5.0 )( 50 – 200 )

-Espècie o Grup protozoo dominant(GPROTO-DOMI) 13 8 Sessils Crawling P-flag Rotífers Cil-Car Amebes Cap

Filamentós Predominant (FILAM) 5 10 Zooglea Microthrix 021/thiotrix 0041 Noc/0041 Nocardia Cap

Case-Based Reasoning System

CASE LIBRARY SEEDEDWITH 24 CASES

# C A S D IA S IT U A C IÓ # C A S D IA S IT U A C IÓ

1 2 7 /2 /9 6 N o rm a l H iv e rn 1 3 8 /8 /9 6 N o rm a l E s tiu

2 3 /1 /9 7 P lu ja 1 4 1 0 /1 /9 7 P lu ja

3 9 /1 2 /9 6 T em p es ta 1 5 5 /9 /9 6 D es f lo cu la c ió

4 3 /7 /9 7 N o rm a l E s t iu 1 6 2 8 /1 1 /9 6 N o rm a l H iv ern

5 1 2 /9 /9 6 X o c d e C à r reg a 1 7 2 5 /8 /9 6 N o rm a l E s tiu

6 1 1 /1 2 /9 6 B a ix a C à r reg a 1 8 2 7 /1 1 /9 7 M ic o th r ix iZ o o g lea

7 6 /1 1 /9 7 D es n itr if ica c ió 1 9 2 8 /1 0 /9 7 X o c d e C lo ru rs

8 3 /1 0 /9 6 D eca n .1 ª d e fic ien t 2 0 3 /6 /9 7 F o a m in g p e rN o ca rd ia

9 7 /5 /9 6 B u lq u in g p e rT h io th r ix 2 1 2 3 /1 /9 7 T erb o les a p er p -

fla g e lta s

1 0 2 5 /3 /9 7 F o a m in g p e rM ic o th r ix 2 2 2 0 /1 1 /9 7 N itr ifica c ió

1 1 2 3 /1 /9 6 F o a m in g p e rM ic o th r ix 2 3 1 6 /1 2 /9 7 T em p es ta

1 2 1 5 /4 /9 7 F o a m in g 2 4 2 5 /4 /9 6T ra n s ic ió d es itu a c ió n o rm a l aB u lq u in g

B

B

N

A NA A

N

N

Cas 14

N

A A N

N

Noc/Thio

Cas 11

Cas 21Cap

B

Cas 2

Cas 18

N

NBN

N B

Cas 3B

N

N

N

Cas 22

B

N

Cas 6 Cas 16B N Cas 5

A

ZooCas 19

N

Cas 12 Cas 10Cap

N

Cas 25

N

Crawling

Mico Noc

Cas 13 Cas 7N A B

A

Noc/Thio

?

Cas 1

Cas 15N

B

Cas 8 A

Mico

A

N

N

NCas 9

?

?

Cil-Car

A

CASE LIBRARY SEEDED WITH 24 CASES

Cas 23

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Ontologies

Issues To obtain a unified (but multilingual), complete and

coherent terminology for a domain Knowledge reuse and sharing To help in the management of problematic situations:

impasses

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Ontologies

Concepts organized in taxonomies, linked by relations and conforming to axioms.

Axioms can be very general, e.g.: “foxes eat fish”


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Integration and cooperation of differenttechniques

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DAI-DEPUR: a hybrid AI framework (1)

DOMAIN THEORY / MODELS PROCESSES / METHODS

IDENTIFIED SITUATION(S)

OFF-LINE VALUESDATA GATHERING

ADOPTED SOLUTION

OPERATOR’S VALIDATION

COMBINATION

MODEL-BASED REASONING

RULE-BASED REASONING

CASE-BASED REASONING

KNOWLEDGE ACQUISITION

LEARNING

EXPERT KNOWLEDGE

EXPERIENTIAL KNOWLEDGE

NUM. CONTROL KNOWLEDGE

PREDICTIVE KNOWLEDGE

SPECIFIC SITUATION(S)

GENERAL SITUATION(S)

PROPOSED SOLUTION(S)

EXPERIENTIAL/ EXPERT

ACTUATION

NUMERICALCONTROL

ACTUATION

ACTUATION

VALIDATION

SUPERVISION

PREDICTION

DIAGNOSIS

ADAPTATION

EVALUATIONDATAON-LINE SENSORS

KNOWLEDGE / EXPERTISE

SITUATIONS

PLANS

SUPERVISORY CYCLE

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Numerical control knowledge

Normal situations

Numerical computation

Mathematical control algorithm For instance, DO (Dissolved Oxygen) Predictive control

algorithm for WWTP

Optimisation of process

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Operation of the Supervisory Cycle

Data Base Information Gathering

The Supervisory Module is activated at the beginning or the end of the day or when

the user requirement

File Importation with average values of the day before

ACTIVATION of both systems in PARAL·LEL

CASE-BASED SYSTEM RULE-BASED SYSTEM

WWTP MODELS: GPS-X / T.N.

PREDICTION

ADAPTACIÓ

RECUPERACIÓ

Bibliotecade casos

Noucas

Millorcas

Casadaptat

Diagnosis & actuation proposal from adapted CASE

Information Combination and situation inferenceSUPERVISORY MODULE

Diagnosis & Actuation inferred

REGLES i ARBRESde DECISIÓ

ACTUATIONPLAN proposal

WWTP

APRENENTATGE

AVALUACIÓ

PLANT MANAGER

EXPERT, EXPERIENTIAL ACTUATION OR by NUMERICAL CONTROL


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Decision Making Support

Transition Networks, Numerical simulations

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Predictive knowledge

Transitions between situations

Practical experiented knowledge

Transition networks: model-based reasoning

Prediction

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The Transition Network Model

S1

S3

S2

S0

I4, C1

I2, C4

I1, C5

I2, C3

I3, C6

I4, C1

I2, C5

I2, C1

I2, C4

I3, C6

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Part of a transition networkBad sedimentability transitions

S1S3

(I1,C1)

(I1,C1)

(I1,C1)

(I1,C1)S2

(I1,C2)

(I1,C1)

(I2,C1)

(I2,C1)

(I1,C2)

(I4,C1)(I2,C1)


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Actuation over the system

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Actuation

Off-line Actuation Operator Validation

On-line Actuation Automatic Actuation


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Learning

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Learning by Experience

Learning new problems and new solutions

Improve the problem solving process

Improve the diagnosis process

Avoid making the same mistakes

System increases performance with time


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PRINCIPLES OF ARTIFICIAL INTELLIGENCE

Fundamentals of AI

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Artificial Intelligence (1)

E. Charniak & D. McDermott, 1985 Artificial Intelligence is the study of mental faculties through

the use of computational models.

E. Rich & K. Knight, 1991 Artificial Intelligence (AI) is the study of how to make

computers do things, which, at the moment, people do better

L. Steels, 1993 Artificial Intelligence is a scientific research field concerned

with intelligent behaviour.

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Artificial Intelligence (2) OMNI:

What is this the main goal of AI?

Herbert A. Simon (1994):AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind.

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Artificial Intelligence (3)

Artificial Intelligence is the study of the possible or existing mechanisms –in human or other beings–providing such behaviour in them that can be considered as intelligence, and the simulation of these mechanisms, named as cognitive tasks, in a computer through the computer's programming.

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Artificial Intelligence Areas by application field Puzzle Resolution

Automatic Theorem Proving/Logic Programming

Game Theory

Medical Diagnosis

Machine Translation

Symbolic Mathematics and Algebra

Robotics

Fault Diagnosis

Text Understanding and Generation

Monitoring and Control Systems

e-Commerce

Business Intelligence

Intelligent Decision Support Systems

Recommender Systems

Intelligent Web Services

Social Networks Analysis

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Artificial Intelligence Areas by Cognitive Tasks

Vision and Perception

Natural language understanding

Knowledge acquisition

Knowledge representation

Reasoning

Search

Planning

Explanation

Learning

Motion

Speech and Natural language generation

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AI Goals The construction of Intelligent Systems The construction of such intelligent systems or intelligent

agents means that the agents must show an intelligent behaviour like: Autonomy Learning skills Communication abilities Coordination abilities Collaboration abilities with other agents, either humans or

artificial ones.

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Human Beings or “Intelligent” Agents BehaviourEXTERNAL STIMULIVisionNatural Language Understanding

INTERNAL STIMULI

INTERNAL COGNITIVE TASKS INTERNAL REACTION ACTIONSReasoning, Know. Acquisition, Search, Learning, …

Human being Goals Knowledge

EXTERNAL REACTION ACTIONSMotionNatural Language Generation

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Intelligent Agents

An intelligent agent is a computer system that is capable of flexible autonomous action in order to meet its design objectives (Wooldridge and Jennings, 1995).

Agents must be: Responsive: agents should perceive their environment and

respond in a timely fashion to changes that occur in it, Proactive: agents should be able to exhibit opportunistic,

goal-directed behaviour and take the initiative where appropriate

Social: agents should be able to interact, when they deem appropriate, with other artificial agents and humans in order to complete their own problem solving capabilities and to help others with their activities.

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ExamplesAdapted from [Russell & Norvig 2000]

Agent Type Goals / Performance Measure Environment Sensors Actuators

Medical Diagnosis System Healthy patient, Reduced costs

Patient, hospital, staff Keyboard entry of symptoms, findings, patient’s answer

Display of questions, tests, diagnoses, treatments, referrals

Satellite image analysis system

Correct image categorization

Images from orbiting satellite

Colour pixel arrays Display of scene categorization

Internet Softbot Summarise relevant information

Users, the web Web pages, text, links Interesting topic detection in web pages, going to other web pages

Machine Translation system

Text translation from one language to another

Text, Linguistic knowledge Text Sentence translation, Semantic interpretation of paragraphs

MARS Pathfinder Maximum information gathering from Mars

Mars surface, Earth signals Telemetry, Mars environment images, Velocity and balance information

Forward, backward, accelerate, braking, turning, take one photo

Refinery controller Purity, yield, safety Refinery, operators Temperature, pressure, chemical sensors

Valves, pumps, heaters, displays

Interactive English tutor Student’s score on test satisfactory

Set of students, testing agency

Keyboard entry Display of exercises, suggestions, corrections

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Kinds of Agents

Cognitive or deliberative agents: They are based in a deep knowledge representation. Rational actions are implemented through inference

processes by means of rule sets. For instance, a medical diagnosis system built up on a

deep medical knowledge, which provides a deliberative reasoning process.

Reactive or behavioural agents: They are a set of behaviours, which interact and evolve

to reach their goals. They are prepared to react to external stimuli. For instance, a mobile robot, which interacts with the

environment showing emergent behaviours.

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A Cognitive Agent: MYCIN

Medical Diagnosis Infeccious Breathing diseases Great Medical Knowledge Compilation process

Actuation Questions and answers with the doctor

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MYCIN: Example Dialogue (I)

>What is the patient’s name? John Smith >Age? He is 55 {Natural Langiage Interpretation}>Have you obtained positive cultures indicating general>type? {Evidence Caption}Yes>Let’s call the most recent culture CULTURE-1. Fromwhat site was CULTURE-1 taken? From the blood>When? June, 21, 2001>Let’s call the first significant organism from this blood>culture ORGANISM-1. Do you know the identity of>ORGANISM-1? No

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MYCIN: Example Dialogue (II)

>Is ORGANISM-1 a rod or a coccus or something else? Rod {Cause Discrimination}>What is the gramstain of ORGANISM-1? Gramnegative> Has John Smith a previous history of alcoholism?No> Is there evidence that the infection has hospitalary origin?Yes> My therapy recommendations will be based on the>following possible identities of the organism(s) that seem>to be significant:the identity of ORGANISM-1 may be

PSEUDOMONAS

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MYCIN: Example Dialogue (III)

>the identity of ORGANISM-2 may be KLEBSIELLA

the identity of ORGANISM-3 may be

ENTEROBACTER

>My preferred therapy recommendation is as follows:

>Give the following in combination:

>GENTAMYCIN

>Dose: 1.7 mg/kg Q8H - IV or IM

>Comments: Modify dose in renal >failure

>CARBENICILLIN {etc.}

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A Reactive Agent: Marvin

Data Monitoring Repeated Supervision of many parameters Alarm Categorization

Actuation / Reaction Data Anomalies Detection Sending Messages to Operators Alarm Detection

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AI differences with Conventional Programming

ARTIFICIALINTELLIGENCE

CONVENTIONALPROGRAMMING

Symbolic processing Numerical processing

Complex Domains “Normal” Domains

Heuristic search/resolution Algorithmic search/resolution

Approximate/suboptimal solutions “Exact”/Optimal solutions

Approximate/Uncertain Information Exact/certain Information

Functional/Logic Programming Procedural/Object-Oriented Programming


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PRINCIPLES OF ARTIFICIAL INTELLIGENCE

AI Paradigms

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AI Paradigms or Approaches

The deliberative/symbolic paradigms Concerned on the processing of symbols rather than

numerical values. Use a latent reasoning mechanism. Most of them are Cognitive-inspired approaches.

The reactive/subsymbolic paradigms Concerned about more numerical computations and providing

nice and intelligent optimizations schemes or function approximation schemes.

No evident reasoning mechanisms are used. Most of them are Bio-inspired approaches.

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Deliberative Approaches (1)

Logic paradigm: based on representing the knowledge about the problem and the domain theory through logical formulas. The main reasoning mechanism is the automatic theorem

proving using the automated resolution process set by Robinson. It is a very general mechanism.

Major techniques are based on Logic Programming.

Heuristic search and planning paradigm: it is based in searching within a space of possible states, starting from the initial state to a final state, where the problem has been solved. The state space is a graph structure to be intelligently explored. Most commonly used techniques are the A* algorithm and

other heuristic search approaches.

© Miquel Sànchez i Marrè, KEMLG, 201189

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Knowledge-Based paradigm: this kind of approach tries to get benefit from the particular knowledge of a concrete domain, which is normally used by experts when facing the problems to be solved. This knowledge is encoded in what has been named as a

Knowledge Base. Most common knowledge bases are implemented as inference rules (IF <conditions> THEN <actions>). The knowledge is explicitly represented by the inference rules.

Main examples of this paradigm are the Expert Systems and the Intelligent Tutoring Systems.

The reasoning mechanisms are the forward and backward reasoning engines.

The technique used here is commonly known as Rule-Based Reasoning (RBR), or sometimes also known as Knowledge-Based System (KBS).


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Model-Based paradigm: this approach is very similar to the Knowledge-based, because the knowledge of a particular domain is used. The difference relies on the fact that the knowledge is implicitly encoded in some kind of model. Most common approaches are causal models reflecting the

causal relationships among several components of a system, or qualitative models reflecting the qualitative relationships among several attributes which are characterizing the domain.

The reasoning process is done through some kind of interpreter of the model and its component relationships.

Model-Based Reasoning (MBR) and Qualitative Reasoning are major techniques using this approach


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Experience–Based paradigm: this approach tries to solve new problems in a domain using the solution given in the past to a similar problem in the same domain (analogical reasoning). Thus, the solved problems constitute the “knowledge” about the domain. As more experienced is the system better performance achieves,

because the experiences (cases or solved problems) are stored in the Case Base.

This way the system is continuously learning to solve new problems.

The technique used in this approach is known as Case-Based Reasoning (CBR) or Instance-Based Reasoning.


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Reactive Approaches (1)

Connectionism paradigm: this approach is inspired by the biological neural networks which are in the brain of many living beings. The model of an Artificial Neural Network (ANN) mimics the

biological neural networks with the interconnection of artificial neurons.

The ANN will produce an output result, as an answer to several input information from the input layer neurons, emulating the neural networks of the brain which propagate signals among all the neurons interconnected.

ANNs are general approximation functions very useful in nonlinear conditions


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Evolutionary Computation paradigm: Evolutionary computation is a bio-inspired approach mimicking selection natural process in biological populations. It uses iterative progress, such as growth or development in a

population. This population is then selected in a guided random search being able to use parallel processing to achieve the desired end.

Such processes are often inspired by biological mechanisms of evolution.

Evolutionary computation provides a biological combinatorial optimization approach.


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Uncertainty reasoning paradigms: A Bayesian network, belief network or directed acyclic

graphical model Is a probabilistic graphical model that represents a set of

random variables and their conditional dependencies via a directed acyclic graph (DAG).

They provide an inference reasoning mechanism to obtain the new probability values of any variable within the network after some new evidences are known

Fuzzy logic systems Systems based on fuzzy logic and possibilistic theory to

model the vagueness and imprecision concepts. The mathematical possibilistic model assigns a possibility

value to each element which is evaluated regarding whether it belongs to a set. Values are evaluated in terms of logical variables that take on continuous values between 0 and 1.


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Other optimization paradigms: there are several optimization techniques approaches ranging from Collective problem solving techniques named as

“Swarm Intelligence” such as Ant colony optimization Swarm particle optimization Etc.

Until another simpler optimization techniques using several meta-heuristics


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Miquel Sànchez i Marrè, Karina Gibert([email protected], [email protected])

http://kemlg.upc.edu/

Documents

Intelligent Decision Support Systems 2-MAI-1112.pdf · Intelligent Decision Support Systems Intelligent Decision Support Systems (Part II - EVOLUTION OF DECISION SUPPORT SYSTEMS