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Multi-Agent Systems. University “Politehnica” of Bucarest Spring 2011 Adina Magda Florea http://turing.cs.pub.ro/mas_11 curs.cs.pub.ro. Course goals.

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multi agent systems

Multi-Agent Systems

University “Politehnica” of BucarestSpring 2011Adina Magda Florea

http://turing.cs.pub.ro/mas_11curs.cs.pub.ro

course goals
Course goals
  • Multi-agent systems (MAS) may be viewed as a collection of distributed autonomous artifacts capable of accomplishing complex tasks through interaction, coordination, collective intelligence and emergence of patterns of behavior.
  • By the end of this course, you will know:
    • what are the basic ideas, models, and paradigms offered by intelligent agents and MAS;
    • build multi-agent systems or select the right MAS framework for solving a problem
    • use the agent technology in different areas of applications
    • what do agents bring  as compared to distributed processing or object oriented software development.
course content
Course content
  • What are agents and MAS?
  • Agent architectures
  • Communication
  • Knowledge representation
  • Distributed planning
  • Coordination
  • Auctions
  • Negotiation
  • Agent oriented programming
  • MAS learning
  • Agents and web services
  • Agents and MAS applications
course requirements
Course requirements
  • Course gradesMid-term exam               20% Final exam                     30% Course activity 10%

Projects  20%Laboratory                    20%

  • Requirements: min 7 lab attendances, min 50% of term activity (mid-term ex, projects, lab)
  • Academic Honesty PolicyIt will be considered an honor code violation to give or use someone else's code or written answers, either for the assignments or exam tests. If such a case occurs, we will take action accordingly.
lecture 1 introduction
Lecture 1: Introduction
  • Motivation for agents
  • Definitions of agents  agent characteristics, taxonomy
  • Agents and objects
  • Multi-Agent Systems
  • Agent’s intelligence
  • Areas of R&D in MAS
  • Exemplary application domains
motivations for agents
Motivations for agents
  • Large-scale, complex, distributed systems: understand, built, manage
  • Open and heterogeneous systems - build components independently
  • Distribution of resources
  • Distribution of expertise
  • Needs for personalization and customization
  • Interoperability of pre-existing systems / integration of legacy systems

6

agent
Agent?

The term agent is used frequently nowadays in:

      • Sociology, Biology, Cognitive Psychology, Social Psychology, and
      • Computer Science  AI
  • Why agents?
  • What are they in Computer Science?
  • Do they bring us anything new in modelling and constructing our applications?
  • Much discussion of what (software) agents are and of how they differ from programs in general

7

slide8
What is an agent (in computer science)?
  • There is no universally accepted definition of the term agent and there is a good deal of ongoing debate and controversy on this subject
  • It appears that the agent paradigm is one necessarily endowed with intelligence.
  • Are all computational agents intelligent?
  • Agent = more often defined by its characteristics - many of them may be considered as a manifestation of some aspect of intelligent behaviour.

8

agent definitions
Agent definitions
  • “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)
  • “An agent is an entity that senses its environment and acts upon it” (Russell, 1997)
slide10
“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)”
  • “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)

10

slide11
“Agent = a hardware or (more usually) a software-based computer system that enjoys the following properties:
  • 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

  • reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it;
  • pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking initiative.”
  • social ability - agents interact with other agents (and possibly humans) via some kind of agent-communication language;

(Wooldridge and Jennings, 1995)

11

slide12
Identified characteristics

Two main streams of definitions

  • Define an agent in isolation
  • Define an agent in the context of a society of agents social dimension MAS

Two types of definitions

  • Does not necessary incorporate intelligence
  • Must incorporate a kind of IA behaviour  intelligent agents

12

slide13
Agents characteristics
  • act on behalf of a user or a / another program
  • autonomous
  • sense the environment and acts upon it / reactivity
  • purposeful action / pro-activity
  • function continuously / persistent software
  • mobility ?
  • Goals, rationality
  • Reasoning, decision making cognitive
  • Learning/adaptation
  • Interaction with other agents - social dimension

Other basis for intelligence?

13

slide14

Agent Environment

Environment properties

- Accessible vs inaccessible

- Deterministic vs

nondeterministic

- Episodic vs non-episodic

- Static vs dynamic

- Open vs closed

Agent

Sensor

Input

Action

Output

Environment

14

slide15

Multi-agent systems

Many entities (agents) in a common environment

Environment

15

Influenece area

Interactions

slide16

MAS - many agents in the same environment

  • Interactions among agents

- high-level interactions

  • Interactions for - coordination

- communication

- organization

  • Coordination

 collectively motivated / interested

 self interested

- own goals / indifferent

- own goals / competition / competing for the same resources

- own goals / competition / contradictory goals

- own goals / coalitions

16

slide17
Communication

 communication protocol

 communication language

- negotiation to reach agreement

- ontology

  • Organizational structures

 centralized vs decentralized

 hierarchical/ markets

"cognitive agent" approach

17

slide18
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:

  • knowledge - John knows humans are mortal
  • beliefs - John took his umbrella because he believed it was going to rain
  • desires, goals - John wants to possess a PhD
  • intentions - John intends to work hard in order to have a PhD
  • choices - John decided to apply for a PhD
  • commitments - John will not stop working until getting his PhD
  • obligations - John has to work to make a living

(Shoham, 1993)

18

slide19
Premises
  • 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.
  • 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?

19

slide20
Comparison with AI - alternate approach of realizing intelligence - the sub-symbolic level of neural networks
  • An alternate model of intelligence in agent systems.

Reactive agents

  • Simple processing units that perceive and react to changes in their environment.
  • Do not have a symbolic representation of the world and do not use complex symbolic reasoning.
  • 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.

20

slide21

Exemplary problems

Player A/ Player B

Defect

Cooperate

Defect

2 , 2

5, 0

Cooperate

0 , 5

3 , 3

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

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.

21

slide22

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

The problem of pray and predators

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

22

slide23
Is intelligence the only optimal action towards a a goal? Only rational behaviour?

Emotional agents

  • A computable science of emotions
  • Virtual actors
    • Listen trough speech recognition software to people
    • Respond, in real time, with morphing faces, music, text, and speech
  • 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
  • Manifest temperament control of emotions

23

slide24

MAS links with other disciplines

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

24

areas of r d in mas
Areas of R&D in MAS
  • 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
  • Norms
  • Trust and reputation

25

areas of r d in mas1
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

26

agents in action
Agents in action
  • NASA’s Earth Observing-1 satellite, which began operation in 2000, was recently turned into an autonomous agent testbed.Image Credit: NASA
  • 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.
  • "What happens if you run out of power and you're on the dark side of the planet and the communications systems is having a problem? It's all those combinations that make life exciting," says Steve Chien, principal scientist for automated planning and scheduling at the NASA Jet Propulsion Laboratory in Pasadena, Calif.

27

tac scm
TAC SCM
  • Negotiation was one of the key agent capabilities tested at the conference's Trading Agent Competition. In one contest, computers ran simulations of agents assembling PCs. The agents were operating factories, managing inventories, negotiating with suppliers and buyers, and making decisions based on a range of variables, such as the risk of taking on a big order even if all the parts weren't available. If an agent made an error in judgment, the company could face financial penalties and order cancellations.

28

slide29

Buttler agent

  • 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.

29

slide30

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.
  • http://www.robocup.org/

30

slide31

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.

31

slide32

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.

32

slide33

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.

33

information agents
Information agents

Personal agents (PDA)

  • provide "intelligent" and user-friendly interfaces
  • observethe user and learn user’s profile
  • sort, classify and administrate e-mails,
  • organize and schedule user's tasks
  • in general, agents that automate the routine tasks of the users

Web agents

  • Tour guides Search engines
  • Indexing agents - human indexing
  • FAQ finders - spider indexing
  • Expertise finder

34

agents in elearning
Agents in eLearning

Agents’ role in e-learning

  • Enhance e-learning content and experience
    • give help, advice, feedback
    • act as a peer learning
    • participate in assessments
    • participate in simulation
    • personalize the learning experience
  • Enhance LMSs
    • facilitate participation
    • facilitate interaction
    • facilitate instructor’s activities

35

agents for e commerce
Agents for e-Commerce

E-commerce

  • Transactions - business-to-busines (B2B)

- business-to-consumer (B2C)

- consumer-to-consumer (C2C)

Difficulties of eCommerce

  • Trust
  • Privacy and security
  • Billing
  • Reliability

36