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Inaldo Costa 1 Agentes de Software Agentes e Sistemas Multiagentes Baseado no livro An Introduction to MultiAgent Systems Michael Wooldridge, John Wiley.

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Presentation on theme: "Inaldo Costa 1 Agentes de Software Agentes e Sistemas Multiagentes Baseado no livro An Introduction to MultiAgent Systems Michael Wooldridge, John Wiley."— Presentation transcript:

1 Inaldo Costa 1 Agentes de Software Agentes e Sistemas Multiagentes Baseado no livro An Introduction to MultiAgent Systems Michael Wooldridge, John Wiley & Sons, 2002.

2 Inaldo Costa 2 Overview Five ongoing trends have marked the history of computing: ubiquity; interconnection; intelligence; delegation; and human-orientation

3 Inaldo Costa 3 Ubiquity The continual reduction in cost of computing capability has made it possible to introduce processing power into places and devices that would have once been uneconomic As processing capability spreads, sophistication (and intelligence of a sort) becomes ubiquitous What could benefit from having a processor embedded in it…?

4 Inaldo Costa 4 Houaiss ubíquo {verbete} Acepções adjetivo 1 que está ou existe ao mesmo tempo em toda parte; onipresente 2 Derivação: por extensão de sentido. que se difundiu extensamente; geral, universal Antônimos ausente, inexistente, limitado

5 Inaldo Costa 5 Interconnection Computer systems today no longer stand alone, but are networked into large distributed systems The internet is an obvious example, but networking is spreading its ever-growing tentacles… Since distributed and concurrent systems have become the norm, some researchers are putting forward theoretical models that portray computing as primarily a process of interaction

6 Inaldo Costa 6 Intelligence The complexity of tasks that we are capable of automating and delegating to computers has grown steadily If you dont feel comfortable with this definition of intelligence, its probably because you are a human

7 Inaldo Costa 7 Delegation Computers are doing more for us – without our intervention We are giving control to computers, even in safety critical tasks One example: fly-by-wire aircraft, where the machines judgment may be trusted more than an experienced pilot Next on the agenda: fly-by-wire cars, intelligent braking systems, cruise control that maintains distance from car in front…

8 Inaldo Costa 8 Human Orientation The movement away from machine-oriented views of programming toward concepts and metaphors that more closely reflect the way we ourselves understand the world Programmers (and users!) relate to the machine differently Programmers conceptualize and implement software in terms of higher-level – more human-oriented – abstractions

9 Inaldo Costa 9 Programming progression… Programming has progressed through: machine code; assembly language; machine-independent programming languages; sub-routines; procedures & functions; abstract data types; objects; to agents.

10 Inaldo Costa 10 Global Computing What techniques might be needed to deal with systems composed of processors? Dont be deterred by its seeming to be science fiction Hundreds of millions of people connected by once seemed to be science fiction… Lets assume that current software development models cant handle this…

11 Inaldo Costa 11 Where does it bring us? Delegation and Intelligence imply the need to build computer systems that can act effectively on our behalf This implies: The ability of computer systems to act independently The ability of computer systems to act in a way that represents our best interests while interacting with other humans or systems

12 Inaldo Costa 12 Interconnection and Distribution Interconnection and Distribution have become core motifs in Computer Science But Interconnection and Distribution, coupled with the need for systems to represent our best interests, implies systems that can cooperate and reach agreements (or even compete) with other systems that have different interests (much as we do with other people)

13 Inaldo Costa 13 So Computer Science expands… These issues were not studied in Computer Science until recently All of these trends have led to the emergence of a new field in Computer Science: multiagent systems

14 Inaldo Costa 14 Agents, a Definition An agent is a computer system that is capable of independent action on behalf of its user or owner

15 Inaldo Costa 15 Multiagent Systems, a Definition A multiagent system is one that consists of a number of agents, which interact with one-another In the most general case, agents will be acting on behalf of users with different goals and motivations To successfully interact, they will require the ability to cooperate, coordinate, and negotiate with each other, much as people do

16 Inaldo Costa 16 Agent Design, Society Design The course covers two key problems: How do we build agents capable of independent, autonomous action, so that they can successfully carry out tasks we delegate to them? How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out those delegated tasks, especially when the other agents cannot be assumed to share the same interests/goals? The first problem is agent design, the second is society design (micro/macro)

17 Inaldo Costa 17 Multiagent Systems In Multiagent Systems, we address questions such as: How can cooperation emerge in societies of self-interested agents? What kinds of languages can agents use to communicate? How can self-interested agents recognize conflict, and how can they (nevertheless) reach agreement? How can autonomous agents coordinate their activities so as to cooperatively achieve goals?

18 Inaldo Costa 18 Multiagent Systems While these questions are all addressed in part by other disciplines (notably economics and social sciences), what makes the multiagent systems field unique is that it emphasizes that the agents in question are computational, information processing entities.

19 Inaldo Costa 19 The Vision Thing Its easiest to understand the field of multiagent systems if you understand researchers vision of the future Fortunately, different researchers have different visions The amalgamation of these visions (and research directions, and methodologies, and interests, and…) define the field But the fields researchers clearly have enough in common to consider each others work relevant to their own

20 Inaldo Costa 20 Spacecraft Control When a space probe makes its long flight from Earth to the outer planets, a ground crew is usually required to continually track its progress, and decide how to deal with unexpected eventualities. This is costly and, if decisions are required quickly, it is simply not practicable. For these reasons, organizations like NASA are seriously investigating the possibility of making probes more autonomous giving them richer decision making capabilities and responsibilities. This is not fiction: NASAs DS1 has done it!

21 Inaldo Costa 21 Deep Space 1 Deep Space 1 launched from Cape Canaveral on October 24, During a highly successful primary mission, it tested 12 advanced, high-risk technologies in space. In an extremely successful extended mission, it encountered comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, – NASA Web site

22 Inaldo Costa 22 Autonomous Agents for specialized tasks The DS1 example is one of a generic class Agents (and their physical instantiation in robots) have a role to play in high-risk situations, unsuitable or impossible for humans The degree of autonomy will differ depending on the situation (remote human control may be an alternative, but not always)

23 Inaldo Costa 23 Air Traffic Control A key air-traffic control system…suddenly fails, leaving flights in the vicinity of the airport with no air-traffic control support. Fortunately, autonomous air-traffic control systems in nearby airports recognize the failure of their peer, and cooperate to track and deal with all affected flights. Systems taking the initiative when necessary Agents cooperating to solve problems beyond the capabilities of any individual agent

24 Inaldo Costa 24 Internet Agents Searching the Internet for the answer to a specific query can be a long and tedious process. So, why not allow a computer program an agent do searches for us? The agent would typically be given a query that would require synthesizing pieces of information from various different Internet information sources. Failure would occur when a particular resource was unavailable, (perhaps due to network failure), or where results could not be obtained.

25 Inaldo Costa 25 What if the agents become better? Internet agents need not simply search They can plan, arrange, buy, negotiate – carry out arrangements of all sorts that would normally be done by their human user As more can be done electronically, software agents theoretically have more access to systems that affect the real-world But new research problems arise just as quickly…

26 Inaldo Costa 26 Research Issues How do you state your preferences to your agent? How can your agent compare different deals from different vendors? What if there are many different parameters? What algorithms can your agent use to negotiate with other agents (to make sure you get a good deal)? These issues arent frivolous – automated procurement could be used massively by (for example) government agencies The Trading Agents Competition…

27 Inaldo Costa 27 Multiagent Systems is Interdisciplinary The field of Multiagent Systems is influenced and inspired by many other fields: Economics, Philosophy, Game Theory, Logic, Ecology, Social Sciences This can be both a strength (infusing well-founded methodologies into the field) and a weakness (there are many different views as to what the field is about) This has analogies with artificial intelligence itself

28 Inaldo Costa 28 Some Views of the Field Agents as a paradigm for software engineering: Software engineers have derived a progressively better understanding of the characteristics of complexity in software. It is now widely recognized that interaction is probably the most important single characteristic of complex software Over the last two decades, a major Computer Science research topic has been the development of tools and techniques to model, understand, and implement systems in which interaction is the norm

29 Inaldo Costa 29 Some Views of the Field Agents as a tool for understanding human societies: Multiagent systems provide a novel new tool for simulating societies, which may help shed some light on various kinds of social processes. This has analogies with the interest in theories of the mind explored by some artificial intelligence researchers

30 Inaldo Costa 30 Objections to MAS Isnt it all just Distributed/Concurrent Systems? There is much to learn from this community, but: Agents are assumed to be autonomous, capable of making independent decision – so they need mechanisms to synchronize and coordinate their activities at run time Agents are (can be) self-interested, so their interactions are economic encounters

31 Inaldo Costa 31 Objections to MAS Isnt it all just AI? We dont need to solve all the problems of artificial intelligence (i.e., all the components of intelligence) in order to build really useful agents Classical AI ignored social aspects of agency. These are important parts of intelligent activity in real-world settings


33 Inaldo Costa 33 What is an Agent? The main point about agents is they are autonomous: capable of acting independently, exhibiting control over their internal state Thus: an agent is a computer system capable of autonomous action in some environment in order to meet its design objectives SYSTEM ENVIRONMENT input output

34 Inaldo Costa 34 What is an Agent? Trivial (non-interesting) agents: thermostat UNIX daemon (e.g., biff) An intelligent agent is a computer system capable of flexible autonomous action in some environment By flexible, we mean: reactive pro-active social

35 Inaldo Costa 35 Reactivity If a programs environment is guaranteed to be fixed, the program need never worry about its own success or failure – program just executes blindly Example of fixed environment: compiler The real world is not like that: things change, information is incomplete. Many (most?) interesting environments are dynamic Software is hard to build for dynamic domains: program must take into account possibility of failure – ask itself whether it is worth executing! A reactive system is one that maintains an ongoing interaction with its environment, and responds to changes that occur in it (in time for the response to be useful)

36 Inaldo Costa 36 Proactiveness Reacting to an environment is easy (e.g., stimulus response rules) But we generally want agents to do things for us Hence goal directed behavior Pro-activeness = generating and attempting to achieve goals; not driven solely by events; taking the initiative Recognizing opportunities

37 Inaldo Costa 37 Balancing Reactive and Goal-Oriented Behavior We want our agents to be reactive, responding to changing conditions in an appropriate (timely) fashion We want our agents to systematically work towards long- term goals These two considerations can be at odds with one another Designing an agent that can balance the two remains an open research problem

38 Inaldo Costa 38 Social Ability The real world is a multi-agent environment: we cannot go around attempting to achieve goals without taking others into account Some goals can only be achieved with the cooperation of others Similarly for many computer environments: witness the Internet Social ability in agents is the ability to interact with other agents (and possibly humans) via some kind of agent- communication language, and perhaps cooperate with others

39 Inaldo Costa 39 Other Properties Other properties, sometimes discussed in the context of agency: mobility: the ability of an agent to move around an electronic network veracity: an agent will not knowingly communicate false information benevolence: agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it rationality: agent will act in order to achieve its goals, and will not act in such a way as to prevent its goals being achieved at least insofar as its beliefs permit learning/adaption: agents improve performance over time

40 Inaldo Costa 40 Agents and Objects Are agents just objects by another name? Object: encapsulates some state communicates via message passing has methods, corresponding to operations that may be performed on this state

41 Inaldo Costa 41 Agents and Objects Main differences: agents are autonomous: agents embody stronger notion of autonomy than objects, and in particular, they decide for themselves whether or not to perform an action on request from another agent agents are smart: capable of flexible (reactive, pro-active, social) behavior, and the standard object model has nothing to say about such types of behavior agents are active: a multi-agent system is inherently multi-threaded, in that each agent is assumed to have at least one thread of active control

42 Inaldo Costa 42 Objects do it for free… agents do it because they want to agents do it for money

43 Inaldo Costa 43 Agents and Expert Systems Arent agents just expert systems by another name? Expert systems typically disembodied expertise about some (abstract) domain of discourse (e.g., blood diseases) Example: MYCIN knows about blood diseases in humans It has a wealth of knowledge about blood diseases, in the form of rules A doctor can obtain expert advice about blood diseases by giving MYCIN facts, answering questions, and posing queries

44 Inaldo Costa 44 Agents and Expert Systems Main differences: agents situated in an environment: MYCIN is not aware of the world only information obtained is by asking the user questions agents act: MYCIN does not operate on patients Some real-time (typically process control) expert systems are agents

45 Inaldo Costa 45 Intelligent Agents and AI Arent agents just the AI project? Isnt building an agent what AI is all about? AI aims to build systems that can (ultimately) understand natural language, recognize and understand scenes, use common sense, think creatively, etc. all of which are very hard So, dont we need to solve all of AI to build an agent…?

46 Inaldo Costa 46 Intelligent Agents and AI When building an agent, we simply want a system that can choose the right action to perform, typically in a limited domain We do not have to solve all the problems of AI to build a useful agent: a little intelligence goes a long way! Oren Etzioni, speaking about the commercial experience of NETBOT, Inc: We made our agents dumber and dumber and dumber…until finally they made money.

47 Inaldo Costa 47 Agents as Intentional Systems When explaining human activity, it is often useful to make statements such as the following: Janine took her umbrella because she believed it was going to rain. Michael worked hard because he wanted to possess a PhD. These statements make use of a folk psychology, by which human behavior is predicted and explained through the attribution of attitudes, such as believing and wanting (as in the above examples), hoping, fearing, and so on The attitudes employed in such folk psychological descriptions are called the intentional notions

48 Inaldo Costa 48 Agents as Intentional Systems The philosopher Daniel Dennett coined the term intentional system to describe entities whose behavior can be predicted by the method of attributing belief, desires and rational acumen Dennett identifies different grades of intentional system: A first-order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires. …A second-order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) both those of others and its own

49 Inaldo Costa 49 Agents as Intentional Systems Is it legitimate or useful to attribute beliefs, desires, and so on, to computer systems?

50 Inaldo Costa 50 Agents as Intentional Systems What objects can be described by the intentional stance? As it turns out, more or less anything can... consider a light switch: But most adults would find such a description absurd! Why is this? It is perfectly coherent to treat a light switch as a (very cooperative) agent with the capability of transmitting current at will, who invariably transmits current when it believes that we want it transmitted and not otherwise; flicking the switch is simply our way of communicating our desires. (Yoav Shoham)

51 Inaldo Costa 51 Agents as Intentional Systems The answer seems to be that while the intentional stance description is consistent,... it does not buy us anything, since we essentially understand the mechanism sufficiently to have a simpler, mechanistic description of its behavior. (Yoav Shoham) Put crudely, the more we know about a system, the less we need to rely on animistic, intentional explanations of its behavior But with very complex systems, a mechanistic, explanation of its behavior may not be practicable As computer systems become ever more complex, we need more powerful abstractions and metaphors to explain their operation low level explanations become impractical. The intentional stance is such an abstraction

52 Inaldo Costa 52 Agents as Intentional Systems The intentional notions are thus abstraction tools, which provide us with a convenient and familiar way of describing, explaining, and predicting the behavior of complex systems Remember: most important developments in computing are based on new abstractions: procedural abstraction abstract data types objects Agents, and agents as intentional systems, represent a further, and increasingly powerful abstraction So agent theorists start from the (strong) view of agents as intentional systems: one whose simplest consistent description requires the intentional stance

53 Inaldo Costa 53 Agents as Intentional Systems This intentional stance is an abstraction tool a convenient way of talking about complex systems, which allows us to predict and explain their behavior without having to understand how the mechanism actually works Now, much of computer science is concerned with looking for abstraction mechanisms (witness procedural abstraction, ADTs, objects,…) So why not use the intentional stance as an abstraction tool in computing to explain, understand, and, crucially, program computer systems? This is an important argument in favor of agents

54 Inaldo Costa 54 Agents as Intentional Systems Other 3 points in favor of this idea: Characterizing Agents: It provides us with a familiar, non-technical way of understanding & explaining agents Nested Representations: It gives us the potential to specify systems that include representations of other systems It is widely accepted that such nested representations are essential for agents that must cooperate with other agents

55 Inaldo Costa 55 Agents as Intentional Systems Post-Declarative Systems: This view of agents leads to a kind of post-declarative programming: In procedural programming, we say exactly what a system should do In declarative programming, we state something that we want to achieve, give the system general info about the relationships between objects, and let a built-in control mechanism (e.g., goal-directed theorem proving) figure out what to do With agents, we give a very abstract specification of the system, and let the control mechanism figure out what to do, knowing that it will act in accordance with some built-in theory of agency (e.g., the well-known Cohen-Levesque model of intention)

56 Inaldo Costa 56 An aside… We find that researchers from a more mainstream computing discipline have adopted a similar set of ideas… In distributed systems theory, logics of knowledge are used in the development of knowledge based protocols The rationale is that when constructing protocols, one often encounters reasoning such as the following: IF process i knows process j has received message m 1 THEN process i should send process j the message m 2 In DS theory, knowledge is grounded given a precise interpretation in terms of the states of a process; well examine this point in detail later

57 Inaldo Costa 57 O que é um Agente? Qualquer entidade (humana ou artificial) que: está imersa ou situada em um ambiente (físico, virtual/simulado) percebe seu ambiente através de sensores (ex. câmeras, microfone, teclado, finger,...) age sobre ele através de atuadores (ex. vídeo, auto-falante, impressora, braços, ftp,...) possui objetivos próprios explícitos ou implícitos escolhe suas ações em função das suas percepções para atingir seus objetivos

58 Inaldo Costa 58 Agentes Algoritmo Básico e Arquiteturas

59 Inaldo Costa 59 Agentes: Algoritmo básico função agenteSimples (percepção) retorna ação memória := atualizaMemória (memória, percepção) ação := escolheMelhorAção(memória) memória := atualizaMemória (memória, ação) retorna ação

60 Inaldo Costa 60 Agentes: Arquiteturas Agente tabela Agente reativo Agente reativo com estado interno Agente cognitivo (baseado em objetivos) Agente otimizador Agente adaptativo autonomia complexidade

61 Inaldo Costa 61 sensores Agente atuadores a m b i e n t e Raciocinador modelo do ambiente Agentes: Arquiteturas De forma bem simplificada, um agente pode ser visto como um mapeamento: seqüência perceptiva => ação

62 Inaldo Costa 62 ambiente sensores atuadores Tabela Percepções ações.. Agente Agente Tabela – é mesmo um agente racional? Limitações Mesmo problemas simples requerem tabelas muito grandes ex. xadrez 30^100 Nem sempre é possível, por ignorância ou questão de tempo, construir a tabela Não há autonomia nem flexibilidade Ambiente acessível, determinista, episódico, estático, discreto e minúsculo!

63 Inaldo Costa 63 Agente sensores atuadores Como está o mundo agora? Que ação devo escolher agora? Regras condição-ação a m b i e n t e Agente Reativo Vantagens e desvantagens Regras condição-ação: representação inteligível, modular e eficiente ex. Se velocidade > 60 então multar Não pode armazenar uma seqüência perceptiva, pouca autonomia Ambientes: Reflexo imprescindível em ambientes dinâmicos Acessível, episódico, pequeno

64 Inaldo Costa 64 Agente reativo com estado interno Desvantagem: pouca autonomia não tem objetivo, não encadeia regras Ambientes: determinista e pequeno Ex. Tamagotchi sensores Agente Como está o mundo agora? Regras condição-ação estado: como o mundo era antes como o mundo evolui atuadores Que devo fazer agora? a m b i e n t e impacto de minhas ações

65 Inaldo Costa 65 sensores Agente Como está o mundo agora? Objetivos como o mundo evolui atuadores Que devo fazer agora? a m b i e n t e impacto de minhas ações Como ele ficará se faço isto? estado: como o mundo era antes Agente cognitivo - baseado em objetivo Vantagens e desvantagens: Mais complicado e ineficiente, porém mais flexível, autônomo Não trata objetivos conflitantes Ambientes: determinista ex.: xeque-mate no xadrez

66 Inaldo Costa 66 Agente Como está o mundo agora? Objetivos como o mundo evolui Que devo fazer agora? A m b i e n t e impacto de minhas ações Como ele ficará se faço isto? Agente Baseado em Objetivo Estado interno: como o mundo era antes percepções + ações Sensores Atuadores

67 Inaldo Costa 67 Agente otimizador (utility based) Ambiente: sem restrição Desvantagem: não tem adaptabilidade Ex. motorista carioca Segurança e velocidade – conflito! ambiente sensores atuadores Agente Como está o mundo agora? Que ação devo escolher agora? Função de Utilidade qual é o impacto de minhas ações como o mundo evolui Este novo mundo é melhor? Como ele ficará se faço isto? estado: como o mundo era antes

68 Inaldo Costa 68 Agente que aprende sensores atuadores Agente Gerador de problemas crítico elemento de aprendizagem avaliação objetivos de aprendizagem elemento de execução (agente) trocas conhecimento a m b i e n t e Ambiente: sem restrição Vantagem: tem adaptabilidade (aprende) Ex. motorista sem o mapa da cidade t t+1 t

69 Inaldo Costa 69 Referências An introduction to multiagent systems Woodridge, Michael - John Willey & Sons, Ltd, 2002

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