ARTIFICIAL INTELLIGENCE - 2012

Jiří BÍLA

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ŘÍ A INŽENÝ

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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).

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

23C)“

„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 Emergent

Situations, ...).

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

II. Control of Complex Systems with hardly available models

Identification of Complex Systems by Artificial Neural Networks Life 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 )

Motivation for the development of Non Conventional Neural

Architectures

The unavailability of

information about the

analyzed system from a

trained network (e.g., a

differential equation,…)

High Complexity and a great number of neural parameters of conventional neural networks (MLP,RBF,…)

(?)fyi A conventional Neural Network ~

Black Boxix

1x

nx my

1y

Non linear dynamic neural units for the parallel manipulator

TRIPOD

n

i

u

u

u

1

),( Wxf

10 x

)(v

yv)( dt(.) dt(.)

2

1

0

1

x

x

x

u

u

u

n

i

x

2x

1x

v

v

),( Wxfy

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

3x

Results of adaptive identification of non linear dynamic of TRIPOD

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

Shodný průběh akčních veličin u1 u2 u3

Průběh délky pístů y1 y2 y3

Aproximovaná dynamika délky pístů

Chyba dynamické aproximace

An example of dynamic system with Fuzzy controller

y

w

d

y

e

y

wdu/dt

de/dt

d

-0.4s+1

0.2s +1.2s+12

Transfer Fcn

t

y

Scope3

Scope

PI Fuzzy Logic Controller

Mux

Mux

s

11

Gain

Clock

y

w

d

y

e

y

wdu/dt

de/dt

d

-0.4s+1

0.2s +1.2s+12

Transfer Fcn

t

y

Scope3

Scope

PI Fuzzy Logic Controller

Mux

Mux

s

11

Gain

Clock

An example of dynamic system with Fuzzy controller

y

w

d

y

e

y

wdu/dt

de/dt

d

-0.4s+1

0.2s +1.2s+12

Transfer Fcn

t

y

Scope3

Scope

PI Fuzzy Logic Controller

Mux

Mux

s

11

Gain

Clock

y

w

d

y

e

y

wdu/dt

de/dt

d

-0.4s+1

0.2s +1.2s+12

Transfer Fcn

t

y

Scope3

Scope

PI Fuzzy Logic Controller

Mux

Mux

s

11

Gain

Clock

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.

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.

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.

Trf

statefan

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

Tjf

statefan

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

Tsf6

V515 : TjfV516 : TjfV517 : TjfV518 : TjfV519 : TrfV520 : Trf

STATEsf6()

Design of ICS in Road Tunnel „Mrázovka“ in Prague. Application

of 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

• Class and State diagram for center SF6

TI 5

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

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

<<Interface>>

TI 6

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

<<Interface>>

Trf

casONcasOFFcasON/OFFstatefan

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

Tjf

timeONtimeOFFtimeON/OFFstatefan

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

Tsf6

V515 : TjfV516 : TjfV517 : TjfV518 : TjfV519 : TrfV520 : TrfstateV515 : stringstateV516 : stringstateV517 : stringstateV518 : stringstateV519 : stringstateV520 : string

STATEsf6()

MANUAL CONTROL

entry: Rem : = Manual

START

entry: V520.StartF

CONTROLOFF

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

TUNNEL CLOSED

FIRE

exit: OperClose

GV1

entry: V517.StartFentry: V519.StartF

GV2

entry: V516.StartFentry: V515.StartF

FireZTS

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

OperManual

OperManual

OperManual

OperFire

[ V520.timeON/OFF > TimeRun ]

[ RemVT=0 ]

REV1

entry: V520.StartRev[ RemZT=3 ]

REV2

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

Branching

[ RemZT=0 ]

[ RemZT=7 ]

GV3

entry: 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) ]

Pseudo-code of Delphi type written by special generator

Unit UTsf6;

interfacetype

Tsf6=classprivate

V515:Tjf;V516:Tjf;V517:Tjf;V518:Tjf;V519:Trf;V520:Trf;stateV515:string;stateV516:string;stateV517:string;stateV518:string;stateV519:string;stateV520:string;

//associace: TJetF;//associace: Trf;//associace: Tjf;

publicconstructor Create;procedure STATEsf6;procedure OperGV;procedure OperStart;procedure OperOFF;procedure OperFire;procedure OperClose;procedure OperManual;procedure LocFire;

protectedend;

Implementation

Uses UMainForm;

procedure STATEsf6;BeginEnd;

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;

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.

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: xy (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 „“ !“ ( A)

There are special algorithms for verification of consistency 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 SystemsThe 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, Explnation Modul

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

The support of diagnostics by expert systems

Knowledge base consists of rules of the type:

IF((sp1= qi1 ) AND … AND (spn= qin )) THEN( Porucha pn),

sp1= qi1 means that symptom sp1 has value qi1 .

Classification of symptoms:

- overloading of technological limits (x TM), - analysis of signal morphology,

Example: Detection of faults in energetic system Herbertov

Fault Diagnosis in Herbertov Area

The Exceed of technological limits: (P10 PMAX) AND (T12 TMAX) fault of the valve near the pumps L33 or L35.

Autonomous systems

Artificial intelligence without representation (i.e., without internal model), (Brooksians).

Instead of an internal model - reinforcement of reactivity.

Instead of long learning of human knowledge and habits - the development of adaptive reactive systems with instincts, reflexes and simple complexes of behavior.

The „Intelligence“ is developed in reaction with environment.

Problems of control is transferred into problems of emergent behavior (one of

characteristics of Artificial Life).

Difficulties with learning and representation of knowledge are transformed to difficulties with communication (understanding and the interpretation of activities of the autonomous system).

Rozdíl signálů (bez výpadku a s výpadkem): detekce i lokalizace poruchy

Autonomous systems

Example of autonomous mobot AM (mobile robot) - The Centre of Gerstner (CG), Faculty of Electrical Engineering, CTU in Prague.

Means and strategies of AM CG

Example of searching for the trajectory between obstacles - AM CG

Conclusions

Artificial Intelligence (AI) is not the same as the natural intelligence.

There are the following successful fields of AI nowadays:

In theory:

Approximation disciplines as fuzzy control, neural networks and genetic algorithms.

Contributions to psychology and cognitive science. In practice:

Diagnostics,

Contributions to Computer Aided Engineering (CAE) and other CA… .

Consultation systems,

Autonomous systems (planetar moduls, …).

LITERATURA

1. P.H. Winston: Artificial Intelligence, MIT, Addison-Wesley Publishing Company, London, …, many additions from the first in 1977.2. Banerji, R.: Artificial Intelligence, …3. Nilson, S.: Artificial Intelligence, ...2. J.R. Brooks: Intelligence without representation. AI, No. 47, 1991. s. 139-1595. C. Langton: Artificial Life. Addison-Wesley Pub. Comp Inc., 1989.

6. A. Sloman: Can we design a Mind ? Keynote for AID 02 Conference, 2002. 7. K. Ueda: Emergent Synthesis. Artificial Intelligence in Engineering, No. 15, 2001. s. 319 - 327.

Address:

BÍLA Jiří, Prof. Ing. DrSc.Jiri.Bila@fs.cvut.cz , U 12110 FS ČVUT v Praze, Technická 4, 166 07 Praha 6

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