1. Sisteme multi-agent Universitatea “Politehnica” din Bucuresti anul universitar Adina Magda Florea curs.cs.pub.ro

2. Cerinte Examen final 50% Laborator + teme de laborator 30%
Tema semestru 20%
Minim 6 prezente la laborator

3. Curs 1 Motivatie pentru agenti Definitii agenti Sisteme multi-agent
Inteligenta agentilor
Sub-domenii de cercetare

4. De ce agenti? Sisteme complexe, pe scara larga, distribuite
Sisteme deschise si heterogene – construirea independenta a componentelor
Distributia resurselor
Distributia expertizei
Personalizare
Interoperabilitatea sistemelor/ integrare sisteme software exsitente (legacy systems) 4

5. Agent? Termenul agent este frecvent utilizat in:
Sociologie, biologie, psihologie cognitiva, psihologie sociala si
Stiinta calculatoarelor  IA
Ce sunt agentii?
Ce sunt agentii in stiinta calculatoarelor?
Aduc ceva nou?
Cum difera agentii software de alte programe? 5

6. Definitii ale agentilor in stiinta calculatoarelor
Nu exista o definitie unanim acceptata
De ce este greu de definit?
IA, agenti inteligenti, sisteme multi-agent
Aparent agentii sunt dotati cu inteligenta
Sunt toti agentii inteligenti?
Agent = definit mai mult prin caracteristici, unele pot fi considerate ca manifestari ale unui comportament inteligent 6

7. Definitii agenti
“De cele mai multe ori, oamenii folosesc termenul agent pentru a referi o etitate care functioneaza permanent si autonom intr-un mediu in care exsita alte procese si/sau alti agenti” (Shoham, 1993)
“Un agent este o entitate care percepe mediul in care se afla si actioneaza asupra acestuia” (Russell, 1997)

8. “Agentii inteligenti indeplinesc 3 functii: percep conditiile dinamice din mediu, actioneaza pentru a modifica aceste conditii, si rationeaza pentru a interpreta perceptii, a rezolva probleme, a face inferente, si a stabili ce actiuni sa execute. (Hayes-Roth 1995)”
“Agentii inteligenti sunt entitati software care executa anumite operatii pentru utilizator sau pentru un alt program, cu un anumit grad de independenta sau autonomie si utilizeaza cunostinte si reprezentarea scopurilor utlizatorului” (the IBM Agent) 8

9. “Agent = un sistem (software sau hardware) cu urmatoarele proprietati:
autonomie – agentii opereaza fara interventai directa a utilizatorui si au un anumit control asupra actiunilor si starilor lor;
Actiune autonoma flexibila
reactivitate: agentii percep mediul si reactioneaza corespunzator al schimbarile din acesta;
pro-activitate: agentii, pe langa reactia la schimbarile din mediu, sunt capabili sa urmareasca executia scopurilor si sa actioneze independent;
abilitati sociale – agentii interactioneaza cu alti agenti sau cu utilizatorul pe baza unui limbaj de comunicare.
(Wooldridge and Jennings, 1995) 9

10. Caracteristici identificate
2 directii de definitie
Definirea unui agent izolat
Definirea agentilor in colectivitate  dimensiune sociala  SMA
2 tipuri de definitii
Nu neaparat agenti inteligenti
Include o comportare tipica IA  agenti inteligenti 10

11. Caracteristici agenti
Actioneaza pentru un utilizator sau un program
Autonomie
Percepe mediul si actioneaza asupra lui reactiv
Actiuni pro-active
comportare condusa de scop versus condusa de evenimente?
Caracter social
Functionare continua (persistent software)
Mobilitate ?
inteligenta?
Scopuri, rationalitate
Rationament, luarea deciziilor cognitiv
Invatare/adaptare
Interactiune cu alti agenti – dimensiune sociala
Alte moduri de a realiza inteligenta? 11

12. Mediul agentului Proprietatile mediului - Accesibil vs inaccesibil
- Determinist vs
nondeterminist
- Episodic vs non-episodic
- Static vs dinamic
- Discret/continuu
- Contine sau nu alti agenti
Agent
Sensor
intrare
Actiune
iesire
Mediu 12

13. Sisteme multi-agent Mai multi agenti intr-un mediu comun 13 Mediu
Zona de influenta
Interactiuni

14. SMA – mai multi agenti in acelasi mediu
Interactiuni intre agenti
- nivel inalt
Interactiuni pentru- coordonare
- comunicare
- organizare
Coordonare
 motivati colectiv
 motivati individual
scopuri proprii / indiferenta
scopuri proprii / competitie pentru resurse
scopuri proprii si contradictorii / competitie pentru resurse
scopuri proprii / coalitii 14

15. Structuri organizationale  centralizate vs decentralizate
Comunicare
 protocol
 limbaj
- negociere
- ontologii
Structuri organizationale
 centralizate vs decentralizate
 ierarhie/ piata
abordare "agent cognitiv" 15

16. Agenti cognitivi
Modelul uman al perspectivei asupra lumii  caracterizare agent utilizand reprezentari simbolice si notiuni mentale
knowledge - cunostinte
beliefs - convingeri
desires, goals – dorinte, scopuri
intentions - intentii
commitments - angajamente
obligations - obligatii
(Shoham, 1993)
De ce se utilizeaza aceste notiuni?
Comparatie cu IA 16

17. Agenti reactivi
Unitati simple de prelucrare care percep mediul si reactioneaza la schimbarile din mediu
Nu folosesc reprezentari simbolice sau rationament.
Inteligenta nu este situata la nivel individual ci distribuita in sistem, rezulta din interactiunea entitatilor cu mediu – “emergence” 17

18. 18 Problema inteleptilor Dilema prizonierului
Regele picteaza cate o pata alba si spune ca cel putin o pata este alba
Dilema prizonierului
Rezultatele pentru A si B (in puncte ipotetice) in functie de actiunile fiecaruia
Player A / Player B
Tradeaza
Coopereaza
2 , 2
5 , 0
0 , 5
3 , 3
A king wishing to know which of his three wise men is the wisest, paints a white spot on each of their foreheads, tells them at least one spot is white, and asks each to determine the color of his spot. After a while the smartest announces that his spot is white reasoning as follows: ``Suppose my spot were black. The second wisest of us would then see a black and a white and would reason that if his spot were black, the dumbest would see two black spots and would conclude that his spot is white on the basis of the king's assurance. He would have announced it by now, so my spot must be white."
 In formalizing the puzzle, we don't wish to try to formalize the reasoning about how fast other people reason. Therefore, we will imagine that either the king asks the wise men in sequence whether they know the colors of their spots or that he asks synchronously, ``Do you know the color of your spot" getting a chorus of noes. He asks it again with the same result, but on the third asking, they answer that their spots are white. Needless to say, we are also not formalizing any notion of relative wisdom.
The two players in the game can choose between two moves, either "cooperate" or "defect". The idea is that each player gains when both cooperate, but if only one of them cooperates, the other one, who defects, will gain more. If both defect, both lose (or gain very little) but not as much as the "cheated" cooperator whose cooperation is not returned. The whole game situation and its different outcomes can be summarized by table 1, where hypothetical "points" are given as an example of how the differences in result might be quantified. 18

19. 19  Problema prazilor si vanatorilor     Abordare cognitiva
vanatorii au scopuri, prazile nu
Detectia prazilor
Echipa vanatori, roluri
Comunicare/cooperare 
Abordare reactiva
Prazile emit semnale a caror intensitate scade pe masura cresterii distantei de vanatori
Vanatorii emit semnale care pot fi percepute de alti vanatori
Fiecare vanator este atras de o prada si respins de alt semnal de la un vanator
Problem definition: coordinating the actions of the predators so that they can surround the prey animals as fast as possible, a surrounded prey animal being considered dead.
The prays and the predators (both are agents) move over a space represented in the form of a grid. The objective is for the predators to capture the pray animals by surrounding them as in the figure above. The following hypotheses are laid down:
1. The dimension of the environment is finite
2. The predator and pray animals move at fixed speeds and generally at the same speed.
3. The pray animals move in a random manner by making Brownian movements
4. The predators can use the corner and edges to block a pray animal's path
5. The predators have a limited perception of the world that surrounds them, which means that they can see the prey only if it is in one of the squares at a distance within their field of perception 19

20. Agenti emotionali Inteligenta afectiva Actori virtuali Emotii:
recunoasterea vorbirii
gesturi, sinteza de vorbire
Emotii:
Aprecierea unei situatii sau a unui eveniment: bucurie, suparare;
valoarea unei situatii care afecteaza pe alt agent: bucuros-pentru,, gelos, invidios, suprat-pentru;
Aprecierea unui eveniment viitor: speranta, frica;
Aprecierea unei situatii care confirma o asteptare: satisfactie, dezamagire
Controlarea emotiilor prin temperament 20

21. Legaturi cu alte discipline
Economic
theories
Decision theory
OOP
AOP
Markets
Autonomy
Rationality
Distributed
systems
Communication
MAS
Learning
Mobility
Proactivity
Cooperation
Organizations
Reactivity
Character
Artificial intelligence
and DAI
Sociology
Psychology 21

22. Directii de studiu si cercetare
Arhitecturi agent
Reprezentare cunostinte: sine, alti agenti, lume
Comunicare: limbaje, protocol
Planificare distribuita
Cautare distribuita, coordonare
Luarea deciziilor: negociere, piete de marfuri
Invatare
Structuri organizationale
Implementare:
Programarea agentilor: paradigme, limbaje
Platforme multi-agent
Middleware, mobilitate, securitate 22

23. Directii de studiu si cercetare
Aplicatii
Aplicatii industriale: monitorizarea in timp real si managementul proceselor de productie, coordonare retele de calculatoare si de telecomunicatii, sisteme de transport, sisteme de distributie a electricitatii, etc.
Managementul proceselor de business, suport al deciziei
Agenti ofertanti de servicii Web
Agenti pe Grid
eCommerce, eMarkets
Regasirea si filtrarea informatiilor
Interactiunea om-calculator, jocuri
Invatare asistata
PDAs 23

24. Exemple de agenti Buttler agent 24
Imagine your very own mobile butler, able to travel with you and organise every aspect of your life from the meetings you have to the restaurants you eat in.
The program works through mobile phones and is able to determine users' preferences and use the web to plan business and social events
And like a real-life butler the relationship between phone agent and user improves as they get to know each other better.
The learning algorithms will allow the butler to arrange meetings without the need to consult constantly with the user to establish their requirements. 24

25. NASA agents
NASA uses autonomous agents to handle tasks that appear simple but are actually quite complex. For example, one mission goal handled by autonomous agents is simply to not waste fuel. But accomplishing that means balancing multiple demands, such as staying on course and keeping experiments running, as well as dealing with the unexpected.
NASA’s Earth Observing-1 satellite, which began operation in 2000, was recently turned into an autonomous agent testbed. Image Credit: NASA 25

26. Robocup agents
The goal of the annual RoboCup competitions, which have been in existence since 1997, is to produce a team of soccer-playing robots that can beat the human world champion soccer team by the year 2050. 26

27. Intelligent Small World Autonomous Robots for Micro-manipulation
Swarms
Intelligent Small World Autonomous Robots for Micro-manipulation
A leap forward in robotics research by combining experts in microrobotics, in distributed and adaptive systems as well as in self-organising biological swarm systems.
Facilitate the mass-production of microrobots, which can then be employed as a "real" swarm consisting of up to 1,000 robot clients. These clients will all be equipped with limited, pre-rational on-board intelligence.
The swarm will consist of a huge number of heterogeneous robots, differing in the type of sensors, manipulators and computational power. Such a robot swarm is expected to perform a variety of applications, including micro assembly, biological, medical or cleaning tasks. 27

28. Intelligent IT Solutions
Goal-Directed™ Agent technology.
AdaptivEnterprise™ Solution Suite allow businesses to migrate from traditionally static, hierarchical organizations to dynamic, intelligent distributed organizations capable of addressing constantly changing business demands.
Supports a large number of variables, high variety and frequent occurrence of unpredictable external events. 28

29. True UAV Autonomy
In a world first, truly autonomous, Intelligent Agent-controlled flight was achieved by a Codarra ‘Avatar’ unmanned aerial vehicle (UAV).
The flight tests were conducted in restricted airspace at the Australian Army’s Graytown Range about 60 miles north of Melbourne.
The Avatar was guided by an on-board JACK™ intelligent software agent that directed the aircraft’s autopilot during the course of the mission. 29