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ARTIFICIAL INTELLIGENCE - 2012

Jiří BÍLA

AUTOMAT

ICK

É ŘÍ

ZENÍ A INŽENÝRSKÁ IN

FOR

MATIKA

Ústav přístrojové a řídicí techniky, Fakulta strojní, ČVUT v PrazeTechnická 4 , 166 07 Praha 6 , Tel: 00420 2 2435 2563 , Fax: 00420 2 3116414

Main items of the lecture

1. Artificial Intelligence - State of Art

2. Control of Complex Systems.

3. Pattern Recognition - Computer Vision.

4. Computer Aided ... (CAD, CAPP, CAM, CAQC, ..)

5. HMI - Human Machine Interface

6. Problem Solving.

7. Autonomous systems (planetary modules).

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I. AI - BEGINNING AND EVOLUTION

Motivation and Objectives

Consequences of Cybernetics, Control Theory and Automation „Stabilization of quantities (e.g., stabilization of temperature in this building on 23°C)“

„Stabilization of O2 concentration (in atmosphere)“ ?? …

Understanding to speech, text and patterns„Communication in a natural language.“

„How a robot goes out from a closed kitchen ?“

„How to design a „for ever winning“chess automaton ?“

Modeling of coordination structures (e.g., function of living ecosystems, function of the brain, modeling of the Mind ).Unsolvable problems. E.g., „The method of stabilization of salt concentration in oceans“.

I. AI - STATE OF ART

Control of Complex Systems (neuron models, fuzzy controllers).

Pattern Recognition, special sensors, …, computer vision, intelligent cameras.

Computer Aided (CAD, …, CASE).

Communication „Human- Machine“ (a natural language, …, artificial languages).

Problem Solving by expert systems (Instruction, consultation systems, help to human operators, monitoring, …).

Diagnostics (Fault Detection, …, Detection of Unexpected Situations, ...).

Autonomous systems (…, robots detecting unexploded guns, …)

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II. Control of Complex Systems with hardly available models

Identification of Complex Systems by Artificial Neural NetworksLife Cycle of ANN : Learning (training),Testing, Operation.

Training Sequence (sequence of training pairs)

Fuzzy modeling. Computing with uncertain variables and their values

Linguistic variables and linguistic values (e.g., temperature in the room, low, higher, unpleasant, very high, …)

Fuzzy Controllers.Example of rule: IF(The control error is (Positive and Low) AND (The first derivation of Control error is (Positive and High)) ⇒ THEN (Action is (Negative and Middle))

Identification of mathematical model of a parallel manipulator TRIPOD by a neural network

Deployment of non traditional non

linear dynamic neural units for

identification of dynamics of

parallel manipulator TRIPOD

Parallel manipulator TRIPOD(VVZ J04/98 212200008, …, ČVUT )

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Motivation for the development of Non Conventional Neural Architectures

The unavailability of information about theanalyzed system from atrained network (e.g., adifferential equation,…)

High Complexity and a great number of neuralparameters ofconventional neuralnetworks (MLP,RBF,…)

(?)fyi =A conventional Neural Network~

Black Boxix

1x

nx my

1y

Non linear dynamic neural units for theparallel manipulator TRIPOD

n

i

u

u

u

M

M1

),( Wxf

10=x

)(vφ

yv)(φ∫ dt(.) ∫ dt(.)

=

2

1

0

1

xxxu

u

u

n

i

M

M

x

2x1x

v

v

),( Wxfy ≈′′

Each leg is identified by an autonomous non linear dynamic neural unit HONNU.

3x

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Results of adaptive identification of non linear dynamic ofTRIPOD

Results of identification for the same actions and non equal load ofmanipulator platform

Shodný průběh akčních veličin u1 u2 u3

Průběh délky pístů y1 y2 y3

Aproximovaná dynamika délky pístů

Chyba dynamické aproximace

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III. Pattern Recognition, special sensors, …, computer vision, intelligent cameras.

Development of Special Sensors.

Representation of external world by means of artificial optical and tactile

signals. :

• Sensors for surface pressure (diagnostics of walking), tactile sensors,

sensors of force distribution in material structures (e.g., in over loaded parts

of bones).

Sensor for the measurement of pressure distribution on the surface

The cellular sensor is connectible by a parallel port to

computer and allows to activate 7500 cells 300 times per

second.

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IV. Computer Aided Design (CAD, CAPP,…, CASE).

Classification of design phases : Early Design, Conceptual Design, Detailed Design

Classification of Design activities according to design objectives: Construction and technology (CAD), Design of production phases „in small“ (CA Production Planning), (CAM), Design of production phases „in large“ (CA Manufacturing), Design of assembly phases (CA Assembly), Design of systems for Quality Control (CA Quality Control), … , Design of Software products (CASE -Computer Aided Software Engineering).

Classification of Design according to computer support:Formal approach (Formal logic, expert systems), Deployment of special methodologies and CASE systems, Evolutionary approaches (e.g., gradual adaptation of prototypes. Genetic Algorithms).

IV.Design of Information and Control Systems (ICS).

Design of ICS - without use of special methodologies and SW support - only in simple cases.

„ All designs end by a program“. Description of functions and activities of the program needs a special formal means.

Integration of activities by methodologies and SW support: Concetration of needed knowledge, analysis of information nvironment and controlled system, design of a sceleton of ICS, generation of ICS program code.

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Design of ICS by OMT (Object Modeling Technique, (Rubmbaugh, 1991) and UML (Unified Modeling Language (OMG, 1998)).

The OMT objective: To combine and to connect all important design phases from the description in natural language, trough analysis and of designed ICS till the design of ICS and generation of program code. UML is a Multi-dimensional graphic-symbolical language that continues OMT methodology.UML has 8 modeling strata: Use case model (1), Class Model (2), State Diagram(3), Interaction Diagram (4), Co-operation Diagram (5), Model of Activities (6), Component Model (7), Deployment Model (8).Rough design scheme: Basic description of the problem (Expert) → Structured formulation of the problem (Knowledge Engineer) → OMT methodology → UML model → Implementation (CASE system and code generation) → Maintenance of the program product.

Example of „translation“ of a sentence in natural language into class diagram by OMT:

The sentence: „Center sf6 contains Jet Fans V515, …, V518 with reversation and 2 values control a Jet Fans V519 a V520 with reversation and continuously set up power.

Trfstatefan

SpustitF()SpustitRev()Zastavit()SetUp()staterfan()

Tjfstatefan

SpustitF()SpustitRev()Zastavit()SetUp()statefan()

Tsf6V515 : TjfV516 : TjfV517 : TjfV518 : TjfV519 : TrfV520 : Trf

STATEsf6()

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Design of ICS in Road Tunnel „Mrázovka“ in Prague. Applicationof OMT, UML and Rational Rose CASE.

9 controlled

processes: Large

ventilation, Small

ventilation,

Transport, Security,

Energetics,

Maintanence,

Water sources, ...

Scheme of Road Tunnel Mrázovka in Prague

VTJh oooSF1

VTSr

B

VTSh

ZTS II

AJ

AS

ZTJr

VTJr

M5

M8

o SVK

M6

M7

M9

SF9ooo

M10

M11

M1

M3

oooSF2

M2

oooSF6

ZTS III

oooSF10

oooSF11

p2 o

ooooLSF1

ooooLSF2

Main Outputof Ventilation

N-PORT

oooSF3

SF4ooo

oooSF7

oooSF8

LSF3oooo

ZTJh

M4

o p1

o SVK

A

oLSF4

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• Class and State diagram for center SF6

TI 5Rem : stringRemVT : integerRemZT : integerLocFire : stringt1 : floatt2 : floatIntervalMea : floatdelta : float

OperGV()OperStart()OperOFF()OperFire()OperClose()OperManual()LocFire()

<<Interface>>

TI 6COReq : floatOPReq : floatNOxReq : floatQReq : floatDPReq : floatCOON : floatOPON : floatOPFire : floatCOMain : floatOPMain : floatCOOFF : floatOPOFF : floatCOClose : floatOPClose : float

<<Interface>>

TrfcasONcasOFFcasON/OFFstatefan

StartF()StartRev()Stop()SetUp()timeON/OFF()staterfan()

TjftimeONtimeOFFtimeON/OFFstatefan

StartF()StartRev()Stop()SetUp()timeON/OFF()statefan()

Tsf6V515 : TjfV516 : TjfV517 : TjfV518 : TjfV519 : TrfV520 : TrfstateV515 : stringstateV516 : stringstateV517 : stringstateV518 : stringstateV519 : stringstateV520 : string

STATEsf6()

MANUAL CONTROLentry: Rem : = Manual

STARTentry: V520.StartF

CONTROLOFFdo: t2:= now()do: delta:= t1- t2entry: V519.Stopentry: V520.Stopentry: V515.Stopentry: V516.Stopentry: V517.Stopentry: V518.Stop

TUNNEL CLOSED

FIREexit: OperClose

GV1entry: V517.StartFentry: V519.StartF

GV2entry: V516.StartFentry: V515.StartF

FireZTSentry: V519.StartFentry: V520.StartFentry: V517.SetUpentry: V518.SetUpentry: V515.SetUpentry: V516.SetUp

OperManual

OperManual

OperManual

OperFire

[ V520.timeON/OFF > TimeRun ]

[ RemVT=0 ]

REV1entry: V520.StartRev[ RemZT=3 ]

REV2entry: V519.StartReventry: V517.StartReventry: V516.StartReventry: V515.StartRev

Branching

[ RemZT=0 ]

[ RemZT=7 ]

GV3entry: V518.StartF

[ (RemZT=1)OR(RemZT=2) ]

[ RemZT=8 ]

[ RemZT=0 ]OperFire

[ V517.timeON/FF > TimeRun ]

[ RemZT=3 ]

OperClose

[ (LocFire=VTJH)OR(LocFire=VTJR)OR(LocFire=VTTSr)OR(LocFire=B) ]

[ LocFire=ZTS ]

[ RemZT=7 ]

[ RemZT=0 ]

OperFire

[ V515.timeON/OFF>TimeRun ]

[ V520.timeON/OFF>TimeRun ]

[ RemZT=7 ]

[ RemZT=0 ]

[ RemZT=3 ][ (RemZT=1)OR(RemZT=2) ]

OperManual

[ RemZT=0 ]

OperFire

[ RemZT=3 ]

OperFire [ (RemZT=0)OR(RemZT=3) ]

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Pseudo-code of Delphi type written by special generatorU n it U T s f6 ;

in te rfa cety p e

T sf6= c la ssp riv a te

V 5 1 5 :T jf;V 5 1 6 :T jf;V 5 1 7 :T jf;V 5 1 8 :T jf;V 5 1 9 :T rf;V 5 2 0 :T rf;s ta te V 5 1 5 :s trin g ;s ta te V 5 1 6 :s trin g ;s ta te V 5 1 7 :s trin g ;s ta te V 5 1 8 :s trin g ;s ta te V 5 1 9 :s trin g ;s ta te V 5 2 0 :s trin g ;

//a sso c ia c e : T Je tF ;//a sso c ia c e : T rf ;/ /a sso c ia c e : T jf;

p u b licco n stru c to r C re a te ;p ro ce d u re S T A T E sf6 ;p ro ce d u re O p e rG V ;p ro ce d u re O p e rS ta rt;p ro ce d u re O p e rO F F ;p ro ce d u re O p e rF ire ;p ro ce d u re O p e rC lo se ;p ro ce d u re O p e rM a n u a l;p ro ce d u re L o c F ire ;

p ro te c te de n d ;

Im p lem e nta tio n

U se s U M a in F o rm ;

p ro ce d u re S T A T E sf6 ;B e g inE n d ;

procedure OperGV;Begin

//ze stavového diagramu, MANUAL CONTROL ->

//ze stavového diagramu, START ->

End;

procedure OperStart;Begin

//ze stavového diagramu, MANUAL CONTROL -> STARTV520.SpustitF;

End;

procedure OperOFF;BeginEnd;

procedure OperFire;Begin

//ze stavového diagramu, START -> FIRE

//ze stavového diagramu, GV1 -> FIRE

//ze stavového diagramu, GV2 -> FIRE

//ze stavového diagramu, Branching -> FIRE

//ze stavového diagramu, Initial -> FIRE

//ze stavového diagramu, GV3 -> FIRE

End;

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VI. Problem Solving by Expert Systems.

♦ Expert System contains Knowledge.

♦ Expert System is destined for interaction with human subject.

♦ Expert System contains Knowledge about ill Identifiable processes and objects - unavailable models.

♦ Basic operation for expert system is the Inference (not the computation).

Support of Problem Solving

♦ System of instructions. The system manages a process by commands.

♦Qualitative models of actions„What/IF“.

♦Decision Support.

-Intuitive synthesis. - An ideal form of the support. - Compromising way: Formal logic.

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Support of Problem Solving by Formal Logic

♦The description of all available knowledge that are relevant for the problem, the description of the environment of the problem and of the goal of the problem solution by the language of formal logic of the first order (FOL) or in the language of propositional logic.

Example of the formula: ∀x∃y (P(x,y) ⇒ Q(z)),

(P, Q … predicates, ∀, ∃ … quantifiers, x, y, z … variables, ⇒ … operator of logic implication).

♦The solution algorithm works with the only one partial task: „Verify, please, if the proposed goal formula „A“ is consistent (there are no contradictions) in the set of the problem description „Γ“ !“ (Γ⊥ A)

♦ There are special algorithms for verification of consistency Γ⊥ A, (e.g., Theorem proving resolution Principle of Robinson (1953)).

♦ There were developed special programming languages for SW support of problem solving by Theorem – languages of the type PROLOG, LISP, POP, … .

Support of Problem Solving by Expert Systems♦The description of all available knowledge that are relevant for the problem, the description of the environment of the problem and of the goal of the problem solution is done i some representation language. Very often is used so called rule-based representation:

Rule: IF((C1, .., Cn, w1z, … , wnz, f)) → THEN(D, g(w1a, …, wna)),

C1, .., Cn, conditions, sentences, propositions, w1a, … , wna ... actualized weights, f … interaction function, D … result of

inference, g(w1, …, wn) … the function for computing of the weight of the result

♦ The rules are structuralized in chains, trees, (cycles), i.e. they for a knowledge base.

♦ Basic modules of expert system: Knowledge base, Inference Engine, User Interface,Programme interface, Modul for Knowledge Acquisition, ExplnationModul

The problem is formulated (for ES) as a collection of conditions. After theStart of problem solving process Inference Engine investigates the knowledgebase till the state of satisfaction of the conditions.

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END