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Multi-Agent Systems


Multi-Agent Systems University Politehnica of Bucarest Spring 2010 Adina Magda Florea – PowerPoint PPT presentation

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Title: Multi-Agent Systems

Multi-Agent Systems
  • University Politehnica of BucarestSpring
    2010Adina Magda Florea
  • http//

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
  • 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
  • what do agents bring  as compared to distributed
    processing or object oriented software

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 grades Mid-term exam               20
    Final exam                     30 Projects 
  • Requirements min 7 lab attendances, min 50 of
    term activity (mid-term ex, projects, lab)
  • Academic Honesty Policy It 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
  • Motivation for agents
  • Definitions of agents ? agent characteristics,
  • Agents and objects
  • Multi-Agent Systems
  • Agents intelligence
  • Areas of RD in MAS
  • Exemplary application domains

Motivations for agents
  • Large-scale, complex, distributed systems
    understand, built, manage
  • Open and heterogeneous systems - build components
  • Distribution of resources
  • Distribution of expertise
  • Needs for personalization and customization
  • Interoperability of pre-existing systems /
    integration of legacy systems

  • 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

  • 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
  • The situation is somehow comparable with the one
    encountered when defining artificial
  • Why was it so difficult to define artificial
    intelligence (and we still doubt that we have
    succeeded in giving a proper definition) and
  • Why is it so difficult to define agents and
    multi-agent systems, when some other concepts in
    computer science, such as object-oriented,
    distributed computing, etc., were not so
    resistant to be properly defined.
  • The concept of agent, as the one of artificial
    intelligence, steams from people, from the human
    society. Trying to emulate or simulate human
    specific concepts in computer programs is
    obviously extremely difficult and resist

  • More than 30 years ago, computer scientists set
    themselves to create artificial intelligence
    programs to mimic human intelligent behaviour, so
    the goal was to create an artefact with the
    capacities of an intelligent person.
  • Now we are facing the challenge to emulate or
    simulate the way human act in their environment,
    interact with one another, cooperatively solve
    problems or act on behalf of others, solve more
    and more complex problems by distributing tasks
    or enhance their problem solving performances by

  • It appears that the agent paradigm is one
    necessarily endowed with intelligence.
  • Are all computational agents intelligent?
  • The answer may be as well yes as no.
  • Not to enter a debate about what intelligence is
  • Agent more often defined by its characteristics
    - many of them may be considered as a
    manifestation of some aspect of intelligent

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)

  • 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 users
    goals or desires. (the IBM Agent)

  • 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
  • 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
  • social ability - agents interact with other
    agents (and possibly humans) via some kind of
    agent-communication language
  • (Wooldridge and Jennings, 1995)

  • 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

  • Agents characteristics
  • act on behalf of a user or a / another program
  • autonomous
  • sense the environment and acts upon it /
  • purposeful action / pro-activity
  • goal-directed behavior vs reactive behaviour?
  • function continuously / persistent software
  • mobility ?
  • intelligence?
  • Goals, rationality
  • Reasoning, decision making cognitive
  • Learning/adaptation
  • Interaction with other agents - social dimension
  • Other basis for intelligence?

  • Questions Examples of agents?
  • (are they all agents?) Intelligent?
  • a thermostat with a sensor for detecting room
  • electronic calendar
  • log-in into your computer you are presented with
    a list of email messages sorted by date
  • log-in into your computer you are presented with
    a list of email messages sorted by order of
  • air-traffic control system of country X fails -
    air-traffic controls in the neighboring countries
    deal with affected flights

Agent Environment
Environment properties - Accessible vs
inaccessible - Deterministic vs
nondeterministic - Episodic vs non-episodic -
Static vs dynamic - Open vs closed - Contains
or not other agents
Sensor Input
Action Output
Multi-agent systems
Many entities (agents) in a common environment
Influenece area
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

  • Communication
  • ? communication protocol
  • ? communication language
  • - negotiation to reach agreement
  • - ontology
  • Organizational structures
  • ? centralized vs decentralized
  • ? hierarchical/ markets
  • "cognitive agent" approach
  • MAS systems?
  • Electronic calendars
  • Air-traffic control system

  • Agents vs Objects
  • Autonomy - stronger - agents have sole control
    over their actions, an agent may refuse or ask
    for compensation
  • Flexibility - Agents are reactive, like objects,
    but also pro-active
  • Agents are usually persistent
  • Own thread of control
  • Agents vs MAS
  • Coordination - as defined by designer, no
    contradictory goals
  • Communication - higher level communication than
    object messages
  • Organization - no explicit organizational
    structures for objects
  • No prescribed rational/intelligent behaviour

  • 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)

  • 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

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


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.
  • The problem of pray and predators

  • Cognitive approach
  • Detection of prey animals
  • Setting up the hunting team allocation of roles
  • Reorganisation of teams
  • Necessity for dialogue/communication and for
  • Predator agents have goals, they appoint a leader
    that organize the distribution of work and
    coordinate actions

  • 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

  • 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
  • Respond, in real time, with morphing faces,
    music, text, and speech
  • Emotions
  • Appraisal of a situation as an event joy,
  • 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

MAS links with other disciplines
Economic theories
Decision theory
Distributed systems
Artificial intelligence and DAI
Areas of RD 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
  • Learning
  • Organizational theories
  • Norms
  • Trust and reputation

Areas of RD 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

Agents in action
  • NASAs 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
  • "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.

  • 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.

Exemple de agenti 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

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//

  • Intelligent Small World Autonomous Robots for
  • 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.

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.
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 Armys Graytown Range
    about 60 miles north of Melbourne.
  • The Avatar was guided by an on-board JACK
    intelligent software agent that directed the
    aircrafts autopilot during the course of the

Information agents
  • Personal agents (PDA)
  • provide "intelligent" and user-friendly
  • observe the user and learn users 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

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 instructors activities

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

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