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Modele arhitectura de agenti

Sisteme multi-agent Curs 2 Universitatea “Politehnica” din Bucuresti 2005 - 2006 Adina Magda Florea adina@cs.pub.ro http://turing.cs.pub.ro/blia_06. Modele arhitectura de agenti. Structura conceptuala a agentilor Arhitecturi de agenti cognitivi Arhitecturi de agenti reactivi

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Modele arhitectura de agenti

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  1. Sisteme multi-agentCurs 2Universitatea “Politehnica” din Bucuresti2005 - 2006Adina Magda Floreaadina@cs.pub.rohttp://turing.cs.pub.ro/blia_06

  2. Modele arhitectura de agenti • Structura conceptuala a agentilor • Arhitecturi de agenti cognitivi • Arhitecturi de agenti reactivi • Arhitecturi stratificate

  3. 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

  4. Un agent este situat in mediu • Perpece mediul prin sensori si actioneaza asupra lui prin efectori • Scop: proiectarea unui program – functie care realizeaza corespondenta sensori - efectori Agent = architectura + program • Mediu • accesibil vs. inaccesibil • determinist vs. non-determinist • static vs. dinamic • discret vs. continu 4

  5. 1.2 Modelare agent 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)) Decision component Agent action Perception Execution component component see action Environment env 5

  6. Interaction component inter Modelare agent I = {i1,…,i,..} Mai multi agenti reactivi see : E  P env : E x A1 x … AnP(E) inter : P  I action : P x I  A Agent (A1) Decision component action Agent (A2) Execution Perception Agent (A3) component component action see Environment env 6

  7. Modelare agent Agenti cognitivi Agenti cu stareS = {s1,…,s,…} • action : S x I Ai • next : S x P  S • inter : S x P  I • see : E  P • env : E x A1 x … AnP(E) 7

  8. Modelare agent 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’ 8

  9. Modelare agent 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) 9

  10. Cum modelam? • Iesirea din labirint • Agent reactiv • Agent cognitiv • Agent cognitiv cu utilitate Probleme: • Ce actiuni selectez • Ce se face daca rezultatul actiunilor nu este cunoscut • Cum se iau in considerare schimbarile din mediu 10

  11. 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 11

  12. 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 12

  13. 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) 13

  14. 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 14

  15. Avantaje LPOI Dezavantaje Avem nevoie de un alt model 15

  16. Arhitecturi 2.3 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 • BDI – teoria rationamentului practic - Bratman, 1988 • Rolul intentiilor 16

  17. BDI • Componenta filozofica • Arhitectura software • IRMA - Intelligent Resource-bounded Machine Architecture • PRS - Procedural Reasoning System • Componenta logica • Rao & Georgeff, Wooldrige • (Int Ai )   (Bel Ai) 17

  18. 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 18 actions

  19. Proprietati ale intentiilor • conduc means-end analysis • limiteaza deliberare • persista • influenteaza convingerile 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 19

  20. 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) 20

  21. 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 21

  22. 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 22

  23. 3. Arhitecturi dea genti 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 23

  24. Arhitectura de subsumare • Un TAB este reprezentat de un modul de competenta (c.m.) • Fiecarte c.m. executa un task simplu – comportare concreta • c.m. opereaza in paralel • Nivele inferiroare fata de cele superioare • c.m. la nivele inferioare • c.m. la nivele superioare  subsumtion architecture 24

  25. Competence Module (2) Explore environ Effectors Sensors Output (actions) Input (percepts) Competence Module (1) Move around Competence Module (0) Avoid obstacles Module 1 can monitor and influence the inputs and outputs of Module 2 M1 = move around while avoiding obstacles M0 M2 = explores the environment looking for distant objects of interests while moving around  M1 • Incorporating the functionality of a subordinated c.m. by a higher module is performed using suppressors (modify input signals) and inhibitors (inhibit output) Competence Module (1) Move around Supressor node Inhibitor node Competence Module (0) Avoid obstacles 25

  26. 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 26

  27. 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?) Daca distributia este reala? 27

  28. 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 atunci depun boaba radioactiva si 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) 28

  29. 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 29

  30. 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) 30

  31. TouringMachine • Horizontal layering – 3 niveluri de realizare a actiunilor • Nivel reactiv - set de reguli situatie-actiune rules pt mediu • Nivel planificare - comportare pro-activa - biblioteca de planuri • Nivel modelare - reprezinta mediul, agentul si ceilalti agenti - stabileste scopuri - scopurile sunt trimise nivelului/stratului inferior • Sistem de control 31

  32. Nivel modelare perceptii Subsistem perceptie Nivel planificare Subsistem actiune Nivel reactiv actiuni Subsistem control 32

  33. InteRRaP • Stratificata • BDI Principii • 2 niveluri • Atat controlul cat si BC sunt stratificate • Controlul este bottom-up • Fiecare nivel foloseste rezultatele nivelului inferior Fiecare nivel de control este format din: - modul recunoastere situatie / activare scop (SG) - modul planificare (PS) 33

  34. 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 34 actions percepts

  35. 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 35

  36. 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. 36

  37. 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/ 37

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