1. Sisteme multi-agent Universitatea “Politehnica” din Bucuresti anul universitar 2005-2006 Adina Magda Florea adina@cs.pub.ro http://turing.cs.pub.ro/blia_06 http://turing.cs.pub.ro/blia_06

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

3. De ce agenti? n Sisteme complexe, pe scara larga, distribuite n Sisteme deschise si heterogene – construirea independenta a componentelor n Distributia resurselor n Distributia expertizei n Personalizare n Interoperabilitatea sistemelor/ integrare legacy systems 3

4. 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? 4

5. Definitii ale agentilor in stiinta calculatoarelor n Nu exista o definitie unanim acceptata n De ce este greu de definit? n IA, agenti inteligenti, sisteme multi-agent n Aparent agentii sunt dotati cu inteligenta n Sunt toti agentii inteligenti? n Agent = definit mai mult prin caracteristici, unele pot fi considerate ca manifestari ale unui comportament inteligent 5

6. Definitii agenti n “Most often, when people use the term ‘agent’ they refer to an entity that functions continuously and autonomously in an environment in which other processes take place and other agents exist.” (Shoham, 1993) n “An agent is an entity that senses its environment and acts upon it” (Russell, 1997)

7. n “Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. (Hayes-Roth 1995)” n “Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program, with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user’s goals or desires.” (the IBM Agent) 7

8. n “Agent = a hardware or (more usually) a software-based computer system that enjoys the following properties: â autonomy - agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; Flexible autonomous action â reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it; â pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking initiative.” â social ability - agents interact with other agents (and possibly humans) via some kind of agent-communication language; (Wooldridge and Jennings, 1995) 8

9. Caracteristici identificate 2 directii de definitie n Definirea unui agent izolat n Definirea agentilor in colectivitate  dimensiune sociala  SMA 2 tipuri de definitii n Nu neaparat agenti inteligenti n Include o comportare tipica IA  agenti inteligenti 9

10. Caracteristici agenti n Actioneaza pentru un utilizator sau un program n Autonomie n Percepe mediul si actioneaza asupra lui reactiv n Actiuni pro-active goal-directed behavior vs reactive behaviour? n Caracter social n Functionare continua (persistent software) n mobilitate ?inteligenta? n Scopuri, rationalitate cognitiv n Rationament, luarea deciziilorcognitiv n Invatare/adaptare n Interactiune cu alti agenti – dimensiune sociala Alte moduri de a realiza inteligenta? 10

11. 11 Mediul agentului Agent Mediu Sensor intrare Actiune iesire Proprietatile mediului - Accesibil vs inaccesibil - Determinist vs nondeterminist - Episodic vs non-episodic - Static vs dinamic - Deschis vs inchis - Contine sau nu alti agenti

12. 12 Exemple de agenti? Agenti inteligenti? n Thermostat n Calendar electronic n Lista emails n Sistem de control al traficului aerian

13. 13 Exemple de agenti Buttler agent n 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. n The program works through mobile phones and is able to determine users' preferences and use the web to plan business and social events n And like a real-life butler the relationship between phone agent and user improves as they get to know each other better. n The learning algorithms will allow the butler to arrange meetings without the need to consult constantly with the user to establish their requirements.

14. 14 NASA agents n 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. n NASA’s Earth Observing-1 satellite, which began operation in 2000, was recently turned into an autonomous agent testbed. Image Credit: NASA

15. 15 Robocup agents n 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. n http://www.robocup.org/

16. 16Swarms n Intelligent Small World Autonomous Robots for Micro- manipulation n 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. n 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. n 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.

17. 17 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.

18. 18 True UAV Autonomy n In a world first, truly autonomous, Intelligent Agent-controlled flight was achieved by a Codarra ‘Avatar’ unmanned aerial vehicle (UAV). n The flight tests were conducted in restricted airspace at the Australian Army’s Graytown Range about 60 miles north of Melbourne. n The Avatar was guided by an on-board JACK™ intelligent software agent that directed the aircraft’s autopilot during the course of the mission.

19. Sisteme multi-agent Mai multi agenti intr-un mediu comun Mediu Zona de influenta Interactiuni 19

20. n Interactiuni intre agenti - nivel inalt n Interactiuni pentru- coordonare - comunicare - organizare o Coordonare  motivati colectiv  motivati individual - scopuri proprii / indiferenta - scopuri proprii / competitie pentru resurse - scopuri proprii si contradictorii / competitie pentru resurse - scopuri proprii / coalitii 20 SMA – mai multi agenti in acelasi mediu

21. o Comunicare  protocol  limbaj - negociere - ontologii o Structuri organizationale  centralizate vs decentralizate  ierarhie/ piata "agent cognitiv" abordare "agent cognitiv" 21

22. How do agents acquire intelligence? Cognitive agents The model of human intelligence and human perspective of the world  characterise an intelligent agent using symbolic representations and mentalistic notions: ê knowledge - John knows humans are mortal ê beliefs - John took his umbrella because he believed it was going to rain ê desires, goals - John wants to possess a PhD ê intentions - John intends to work hard in order to have a PhD ê choices - John decided to apply for a PhD ê commitments - John will not stop working until getting his PhD ê obligations - John has to work to make a living (Shoham, 1993) 22

23. Premises n Such a mentalistic or intentional view of agents - a kind of "folk psychology" - is not just another invention of computer scientists but is a useful paradigm for describing complex distributed systems. n The complexity of such a system or the fact that we can not know or predict the internal structure of all components seems to imply that we must rely on animistic, intentional explanation of system functioning and behavior. Is this the only way agents can acquire intelligence? 23

24. n Comparison with AI - alternate approach of realizing intelligence - the sub-symbolic level of neural networks n An alternate model of intelligence in agent systems. Reactive agents n Simple processing units that perceive and react to changes in their environment. n Do not have a symbolic representation of the world and do not use complex symbolic reasoning. n The advocates of reactive agent systems claims that intelligence is not a property of the active entity but it is distributed in the system, and steams as the result of the interaction between the many entities of the distributed structure and the environment. 24

25. 25 The problem of Prisoner's Dilemma Outcomes for actor A (in hypothetical "points") depending on the combination of A's action and B's action, in the "prisoner's dilemma" game situation. A similar scheme applies to the outcomes for B. The wise men problem 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 Player A / Player B DefectCooperate Defect 2, 2 5, 0 Cooperate 0, 53, 3

26. The problem of pray and predators  26   Reactive approach  The preys emit a signal whose intensity decreases in proportion to distance - plays the role of attractor for the predators  Hunters emit a signal which acts as a repellent for other hunters, so as not to find themselves at the same place  Each hunter is each attracted by the pray and (weakly) repelled by the other hunters Cognitive approach  Detection of prey animals  Setting up the hunting team; allocation of roles  Reorganisation of teams  Necessity for dialogue/communication and for coordination  Predator agents have goals, they appoint a leader that organize the distribution of work and coordinate actions

27. n Is intelligence the only optimal action towards a a goal? Only rational behaviour? Emotional agents n A computable science of emotions n Virtual actors –Listen trough speech recognition software to people –Respond, in real time, with morphing faces, music, text, and speech n Emotions: –Appraisal of a situation as an event: joy, distress; –Presumed value of a situation as an effect affecting another: happy-for, gloating, resentment, jealousy, envy, sorry-for; –Appraisal of a situation as a prospective event: hope, fear; –Appraisal of a situation as confirming or disconfirming an expectation: satisfaction, relief, fears-confirmed, disappointment n Manifest temperament control of emotions 27

28. 28 Decision theory Economic theories Sociology Psychology Distributed systems OOP Artificial intelligence and DAI Autonomy Markets Learning Proactivity Reactivity Cooperation Character Communication Mobility Organizations AOP MAS MAS links with other disciplines Rationality

29. Areas of R&D in MAS 29  Agent architectures  Knowledge representation: of world, of itself, of the other agents  Communication: languages, protocols  Planning: task sharing, result sharing, distributed planning  Coordination, distributed search  Decision making: negotiation, markets, coalition formation  Learning  Organizational theories

30. Areas of R&D in MAS  Implementation: –Agent programming: paradigms, languages –Agent platforms –Middleware, mobility, security  Applications –Industrial applications: real-time monitoring and management of manufacturing and production process, telecommunication networks, transportation systems, electricity distribution systems, etc. –Business process management, decision support –eCommerce, eMarkets –Information retrieving and filtering –Human-computer interaction –CAI, Web-based learning- CSCW –PDAs - Entertainment 30