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Sisteme multi-agent Curs 2 Universitatea “Politehnica” din Bucuresti anul universitar 2007-2008 Adina Magda Florea adina@cs.pub.ro http://turing.cs.pub.ro/blia_08 curs.cs.pub.ro. Modele arhitecturale de agenti. Structura conceptuala a agentilor Arhitecturi de agenti cognitivi

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Sisteme multi-agentCurs 2Universitatea “Politehnica” din Bucurestianul universitar 2007-2008Adina Magda Floreaadina@cs.pub.rohttp://turing.cs.pub.ro/blia_08curs.cs.pub.ro

modele arhitecturale de agenti
Modele arhitecturale de agenti
  • Structura conceptuala a agentilor
  • Arhitecturi de agenti cognitivi
  • Arhitecturi de agenti reactivi
  • Arhitecturi stratificate
1 structura conceptuala a agentilor
1. Structura conceptuala a agentilor

1.1 Rationalitatea unui agent

  • Ce inseamna rationalitatea unui agent
  • Cum putem masura rationalitatea unui agent?
  • O masura a performantei

3

slide4
Un agent este situat in mediu
  • Perpece mediul prin sensori si actioneaza asupra lui prin efectori
  • Caracteristicile mediului trebuie luate in considerare
  • Scop: proiectarea unui program – functie care realizeaza corespondenta sensori - efectori

Agent = architectura + program

4

modelare agent reactiv

Modelare agenti

Modelare agent reactiv

E={e1, .., e, ..}

P ={p1, .., p, ..}

A ={a1, .., a, ..}

Agent reactiv

see : E  P

action : P  A

env : E x A  E

(env : E x A P(E))

Componenta

decizie

Agent

action

P

A

Componenta

Componenta

perceptie

executie

see

action

Mediu

env (E)

5

modelare agenti reactivi
Modelare agenti reactivi

A1,…, Ai,..

P1,…, Pi,..

(de obicei identice)

Mai multi agenti reactivi

seei : E  Pi

actioni : Pi Ai

env : E x A1 x … AnP(E)

Agent (A1)

Componenta

decizie

action

Agent (A2)

Componenta

Componenta

Agent (A3)

executie

perceptie

action

see

Mediu

env

6

modelare agent cognitiv
Modelare agent cognitiv

E={e1, .., e, ..}

P ={p1, .., p, ..}

A ={a1, .., a, ..}

S={s1, .., s, ..}

Agent cu stare

see : E  P

next : S x P  S

action : S  A

env : E x A P(E)

Componenta

S

decizie

Agent

action, next

P

A

Componenta

Componenta

perceptie

executie

see

action

Mediu

env (E)

7

modelare agenti cognitivi

Componenta

interactiune

inter

Modelare agenti cognitivi

S1,…, Si,..

A1,…, Ai,..

P1,…, Pi,..

(nu intotdeauna identice)

I = {i1, .., ik,…}

Mai multi agenti cognitivi

seei : E  Pi

nexti : Si x P  Si

actioni : Si x I  Ai

interi : Si I

env : E x A1 x … AnP(E)

Agent (A1)

Componenta

decizie

action, next

Agent (A2)

Componenta

Componenta

Agent (A3)

executie

perceptie

action

see

Mediu

8

env

modelare agent cognitiv1
Modelare agent cognitiv

Agenti cu stare si scopuri

goal : E  {0, 1}

Agenti cu utilitate

utility : E  R

Mediu nedeterminist

env : E x A P(E)

Probabilitatea estimata de un agent ca rezultatul unei actiuni (a) executata in e sa fie noua stare e’

9

modelare agent cognitiv2
Modelare agent cognitiv

Agenti cu utilitate

Utilitatea estimata (expected utility) a unei actiuni a intr-o stare e, dpv al agentului

Principiul utilitatii estimate maxime

Maximum Expected Utility (MEU)

Masura a performantei

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Exemplu

Cum modelam?

  • Curatirea unei camere
    • Agent reactiv
    • Agent cognitiv
    • Agent cognitiv cu utilitate

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2 arhitecturi de agenti cognitivi
2. Arhitecturi de agenti cognitivi

2.1 Comportare rationala

IA si Teoria deciziei

  • IA
  • Teoria deciziei
  • Problema 1 = deliberare/decizie vs. actiune/proactivitate
  • Problema 2 = limitarea resurselor

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Control

Interactions

Information about

itself

- what it knows

- what it believes

- what is able to do

- how it is able to do

- what it wants

environment and

other agents

- knowledge

- beliefs

Communication

General cognitive agent architecture

Reasoner

Other

agents

Planner

Scheduler&

Executor

Output

State

Input

Environment

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2.2 Modele LPOI
  • Reprezentare simbolica + inferente – demonstrarea teoremelor pt a afla ce actiuni va face agentul
  • Abordare declarativa
  • (a)Reguli de deductie

PredicateAt(x,y), Free(x,y), Wall(x,y), Exit(dir), Do(action)

Fapte si axiome despre mediu

At(0,0)

Wall(1,1)

x y Wall(x,y)  Free(x,y)

Reguli de deductie

At(x,y)  Free(x,y+1)  Exit(east)  Do(move_east)

Actualizare automata a starii curente si test pt starea scop

At(0,3)

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slide15
Modele LPOI

(b) Utilizarea calcului situational = descrie schimbari utilizand formalismul logic

  • Situatie = starea rezultata prin executarea unei actiuni

Result(Action,State) = NewState

At(location, situation)

At((x,y), Si)  Free(x,y+1)  Exit(east) 

At((x,y+1), Result(move_east,Si))

Scop At((0,3), _) + actiuni care au condus la scop

means-end analysis

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Avantaje LPOI

Dezavantaje

Avem nevoie de un alt model

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2.3 Arhitecturi BDI
  • Specificatii de nivel inalt
  • Means-end analysis
  • Beliefs (convingeri) = informatii pe care agentul le are despre lume
  • Desires (dorinte) = stari pe care agentul ar vrea sa le vada realizate
  • Intentions (intentii) = dorinte (sau actiuni) pe care agentul s-a angajat sa le indeplineasca
  • Rolul intentiilor

17

slide18

BDI

  • Componenta filozofica

– teoria rationamentului practic - Bratman, 1988

  • Arhitectura software
    • IRMA - Intelligent Resource-bounded Machine Architecture
    • PRS - Procedural Reasoning System
  • Componenta logica
    • Rao & Georgeff, Wooldrige
    • (Int Ai )   (Bel Ai)

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slide19

percepts

Arhitectura BDI

Belief revision

Beliefs

Knowledge

B = brf(B, p)

Opportunity

analyzer

Deliberation process

Desires

D = options(B,D, I)

Intentions

Filter

Means-end

reasonner

I = filter(B, D, I)

Intentions structured

in partial plans

 = plan(B, I)

Library of plans

Plans

Executor

19

actions

slide20
Bucla de control a agentului

B = B0 I = I0D = D0

while true do

get next perceipt p

B = brf(B,p)

D = options(B, D, I)

I = filter(B, D, I)

 = plan(B, I)

execute()

end while

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slide21
Strategii de angajare

(Commitment strategies)

  • Optiune aleasa de agent ca intentie – agentul s-a angajat pentru acea optiune
  • Persistenta intentiilor

Interbare: Cat timp se angajeaza un agent fata de o inetntie?

  • Angajare oarba (Blind commitment)
  • Angajare limitata (Single minded commitment)
  • Angajare deschisa (Open minded commitment)

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slide22

Bucla de control BDI

angajare oarba

B = B0

I = I0 D = D0

while true do

get next perceipt p

B = brf(B,p)

D = options(B, D, I)

I = filter(B, D, I)

 = plan(B, I)

while not (empty() or succeeded (I, B)) do

 = head()

execute()

 = tail()

get next perceipt p

B = brf(B,p)

if not sound(, I, B) then

 = plan(B, I)

end while

end while

Reactivity, replan

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slide23

Bucla de control BDI

angajare limitata

B = B0

I = I0 D = D0

while true do

get next perceipt p

B = brf(B,p)

D = options(B, D, I)

I = filter(B, D, I)

 = plan(B, I)

while not (empty() or succeeded (I, B) or impossible(I, B)) do

 = head()

execute()

 = tail()

get next perceipt p

B = brf(B,p)

if not sound(, I, B) then

 = plan(B, I)

end while

end while

Dropping intentions that are impossible

or have succeeded

Reactivity, replan

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Bucla de control BDI

angajare deschisa

B = B0

I = I0 D = D0

while true do

get next perceipt p

B = brf(B,p)

D = options(B, D, I)

I = filter(B, D, I)

 = plan(B, I)

while not (empty() or succeeded (I, B) or impossible(I, B)) do

 = head()

execute()

 = tail()

get next perceipt p

B = brf(B,p)

D = options(B, D, I)

I = filter(B, D, I)

 = plan(B, I)

end while

end while

ifreconsider(I,B) then

Replan

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slide25
Nu exista o unica arhitectura BDI
  • PRS - Procedural Reasoning System (Georgeff)
  • dMARS
  • UMPRS si JAM – C++ (http://www.marcush.net/IRS/irs_downloads.html)
  • JACK – Java (http://www.agent-software.com.au/shared/home/)
  • JADEX – XML si Java, JADE (http://vsis-www.informatik.uni-hamburg.de/projects/jadex/)
  • JASON – Java (http://jason.sourceforge.net/)

25

slide26
3. Arhitecturi de agenti reactivi

Arhitectura de subsumare - Brooks, 1986

  • (1) Luarea deciziilor = {Task Accomplishing Behaviours}
    • Fiecare comportare (behaviour) = o functie ce realizeaza o actiune
    • TAB – automate finite
    • Implementare: situation action
  • (2) Mai multe comportari pot fi activate in paralel

26

slide27
Arhitectura de subsumare
  • Un TAB este reprezentat de un modul de competenta (c.m.)
  • Fiecarte c.m. executa un task simplu
  • c.m. opereaza in paralel
  • Nivele inferioare au prioritate fata de cele superioare
  • c.m. la nivel inferior monitorizeaza si influenteaza intrarile si iesirile c.m. la nivel superior

 subsumtion architecture

27

slide28

Competence

Module (2)

Explore environ

Effectors

Sensors

Output

(actions)

Input

(percepts)

Competence

Module (1)

Move around

Competence

Module (0)

Avoid obstacles

M1 = move around while avoiding obstacles M0

M2 = explores the environment looking for distant objects of interests while moving around  M1

  • Incoroprarea functionalitatii unui c.m. subordonat de catre un c.m. superior se face prin noduri supresoare (modifica semnalul de intrare) si noduri inhibitoare (inhiba iesirea)

Competence

Module (1)

Move around

Supressor node

Inhibitor node

Competence

Module (0)

Avoid obstacles

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slide29
Comportare

(c, a) – conditie-actiune; descrie comportarea

R = { (c, a) | c  P, a  A} - multimea reguli de comportare

  R x R – relatie binara totala de inhibare

function action( p: P)

var fired: P(R), selected: A

begin

fired = {(c, a) | (c, a)  R and p  c}

for each (c, a)  fired do

if   (c', a')  fired such that (c', a')  (c, a) then return a

return null

end

29

slide30
Ne aflam pe o planeta necunoscuta care contine aur. Mostre de teren trebuie aduse la nava. Nu se stie daca sunt aur sau nu. Exsita mai multi agenti autonomi care nu pot comunica intre ei. Nava transmite semnale radio: gradient al campului

Comportare

(1) Daca detectez obstacol atunci schimb directia

(2) Daca am mostre si sunt la baza atunci depune mostre

(3) Daca am mostre si nu sunt la baza atunci urmez campul de gradient

(4) Daca gasesc mostre atunci le iau

(5) Daca adevarat atunci ma misc in mediu

(1)  (2)  (3)  (4)  (5)

Care sunt premisele pt ca acest comportament sa functioneze? (distributie a aurului?)

30

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Agentii pot comunica indirect:

- Depun si culeg boabe radiocative

- Pot seziza aceste boabe radioactive

(1) Daca detectez obstacol atunci schimb directia

(2) Daca am mostre si sunt la baza atunci depune mostre

(3) Daca am mostre si nu sunt la baza atuncidepun boaba radioactivasi urmez campul de gradient

(4) Daca gasesc mostre atunci le iau

(5) Daca gasesc boabe radioactive atunci iau una si urmez campul de gradient

(6) Daca adevarat atunci ma misc in mediu

(1)  (2)  (3)  (4) (5) (6)

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Layer n

Layer n

Layer n

Layer 2

Layer 2

Layer 2

Layer 1

Layer 1

Layer 1

4. Arhitecturi stratificate

  • Comportare reactiva si pro-activa
  • Cel putin 2 straturi
  • Horizontal layering - i/o horizontal
  • Vertical layering - i/o vertical

Action

output

Action

output

Action

output

perceptual

input

Vertical

Horizontal

perceptual

input

perceptual

input

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Horizontal layering
  • n comportari, n niveluri
  • Comportarea globala poate fi inconsistenta
  • Interactiuni intre niveluri: mn (m = nr actiuni pe nivel)
  • Necesita un sistem de control

Vertical layering

  • Interactiuni intre niveluri m2(n-1)
  • Nu sunt tolerante la defecte (daca un nivel se defecteaza)

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slide34
TouringMachine
  • Horizontal layering – 3 niveluri de realizare a actiunilor
  • Nivel reactiv - set de reguli situatie-actiune pt mediu
  • Nivel planificare

- comportare pro-activa

Nivel modelare

- reprezinta mediul, agentul si ceilalti agenti

  • Sistem de control

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slide35

Nivel modelare

perceptii

Subsistem

perceptie

Nivel planificare

Subsistem

actiune

Nivel reactiv

actiuni

Subsistem control

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slide36
InteRRaP
  • Arhitectura stratificata
  • BDI

Principii

  • 2 niveluri
  • Atat controlul cat si BC sunt stratificate
  • Activare bottom-up si executie top-down
  • Fiecare nivel foloseste rezultatele nivelului inferior

Fiecare nivel de control este format din:

- modul recunoastere situatie / activare scop (SG)

- modul planificare (PS)

36

slide37

Local

planning layer

Cooperative

planning layer

Behavior

based layer

PS

SG

PS

SG

SG

PS

Social KB

I

n

t

e

R

R

a

P

Planning KB

World KB

World interface Sensors Effectors Communication

37

actions

percepts

slide38

Options

Operational primitive

Intentions

Situation

Goals

Beliefs

Cooperative situation

Joint plans

Cooperative goals

Cooperative intents

Social model

Cooperative option

Local plans

Local goals

Local option

Local planning situation

Local intentions

Mental model

Routine/emergency sit.

Reactions

World model

Response

Behavior patterns

Reaction

BDI model in InteRRaP

options

Sensors

filter

SG

Effectors

plan

PS

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BDI Architectures
    • First implementation of a BDI architecture: IRMA
  • [Bratman, Israel, Pollack, 1988] M.E. BRATMAN, D.J. ISRAEL et M. E. POLLACK. Plans and resource-bounded practical reasoning, Computational Intelligence, Vol. 4, No. 4, 1988, p.349-355.
    • PRS
  • [Georgeff, Ingrand, 1989] M. P. GEORGEFF et F. F. INGRAND. Decision-making in an embedded reasoning system, dans Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), 1989, p.972-978.
    • Successor of PRS: dMARS
  • [D'Inverno, 1997] M. D'INVERNO et al. A formal specification of dMARS, dans Intelligent Agents IV, A. Rao, M.P. Singh et M. Wooldrige (eds), LNAI Volume 1365, Springer-Verlag, 1997, p.155-176.
  • Subsumption architecture
  • [Brooks, 1991] R. A. BROOKS. Intelligence without reasoning, dans Actes de 12th International Joint Conference on Artificial Intelligence (IJCAI-91), 1991, p.569-595.

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TuringMachine
  • [Ferguson, 1992] I. A. FERGUSON. TuringMachines: An Architecture for Dynamic, Rational, Mobile Agents, Thèse de doctorat, University of Cambridge, UK, 1992.
  • InteRRaP
  • [Muller, 1997] J. MULLER. A cooperation model for autonomous agents, dans Intelligent Agents III, LNAI Volume 1193, J.P. Muller, M. Wooldrige et N.R. Jennings (eds), Springer-Verlag, 1997, p.245-260.

BDI Implementations

The Agent Oriented Software Group

  • Third generation BDI agent system using a component based approached. Implemented in Java
  • http://www.agent-software.com.au/shared/home/

JASON

  • http://jason.sourceforge.net/

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