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SIF8072 Distributed Artificial Intelligence and Intelligent Agents


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Title: SIF8072 Distributed Artificial Intelligence and Intelligent Agents

SIF8072 Distributed Artificial
IntelligenceandIntelligent Agents
Lecture 1 Introduction
  • http//

Lecturer Sobah Abbas Petersen Email
Lecture Outline
  1. Practical Information
  2. Definition of an Agent
  3. Distributed Artificial Intelligence and
    Multi-agent Systems
  4. Agent Typology
  5. Summary and references

Practical Information - I
  • Course-related information
  • ? Web-page http//
  • Lectures Thursdays, 1500-1700, room R4
  • ? Web-page http//
  • Exercises Mondays, 1700-1900, room R4
  • ? Web-page http//
  • Written Exam Wednesday, 14th May
  • ? Web-page http//
    (past exam papers)

Practical Information - II
  • Curriculum
  • Introduction to MultiAgent Systems by Michael
  • available from TAPIR, price NOK 375
  • (http//
  • Additional Articles
  • List available from http//
  • Exercises and Project
  • 4 mandatory exercises and 1 mandatory project
  • Questions regarding Exercises and Project
  • ? Teaching Assistant Peep Kungas
  • Email

Lecture Plan
Date Lecture
1 16.01 2003 Introduction, Overview and Technology
2 23.01 2003 Multi-agent Interactions
3 30.01 2003 Negotiation
4 06.02.2003 Coordination
5 13.02.2003 Agent Communication Languages
6 20.02.2003 Agent Architectures
7 27.02.2003 Multi-agent Systems Architectures
8 06.03.2003 Agent Theory
9 13.03.2003 Mobile Agents
10 20.03.2003 Agent-oriented Software Engineering
11 27.03.2003 Agent-mediated Electronic Commerce
12 03.04.2003 Summary
Example 1
  • When a space probe makes its long flight from
    Earth to outer planets, a ground crew is usually
    required to continue to track its progress and
    decide how to deal with unexpected eventualities.
    This is costly and, if decisions are required
    quickly, it is simply not practical. For these
    reasons, organisations like NASA are seriously
    investigating the possibility of making the
    probes more autonomous giving them richer
    decision making capabilities and

Example 2
  • 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
    information from various different internet
    information sources.

Example 3
  • After a wet and cold winter, you are in need of
    a last minute holiday somewhere warm. After
    specifying your requirements to your Personal
    Digital Assistant (PDA), it converses with a
    number of different web sites which sell services
    such as flights and hotel rooms. After hard
    negotiation on your behalf with a range of sites,
    your PDA presents you with a package holiday.

Overview 1
  • Five ongoing trends have marked the history of
  • Ubiquity
  • Reduction in the cost of computing capability
  • Interconnection
  • Computer systems are networked into large
    distributed systems
  • Intelligence
  • The complexity of tasks that can be automated and
    delegated to computers
  • Delegation
  • Judgement of computer systems are frequently
  • Human-orientation
  • Use concepts and metaphors that reflect how we
    understand the world

Overview 2
  • These trends present major challenges to software
    developers. e.g.
  • Delegation act independently.
  • Intelligence act in a way that represents our
    best interests while interacting with other
    humans or systems.
  • Need systems that can act effectively on our
  • Systems must must have the ability to cooperate
    and reach agreements with other systems.
  • New field Multi-agent Systems

Overview 3
  • An agent is a system that is capable of
    independent action on behalf of its user or
  • A multi-agent system is one that consists of a
    number of agents which interact with one another.
  • In order to successfully interact, agents need
    ability to cooperate, coordinate and negotiate.

Two Key Problems
  1. How do we build agents that are capable of
    independent, autonomous action in order to
    successfully carry out the tasks that we delegate
    to them? (Micro aspects)
  2. How do we build agents that are capable of
    interacting (cooperating, coordinating,
    negotiating) with other agents in order to
    successfully carry out the tasks we delegate to
    them? (Macro aspects)

Fields that inspired agents
  • Artificial Intelligence
  • Agent intelligence, micro aspects
  • Software Engineering
  • Agent as an abstraction
  • Distributed systems and Computer Networks
  • Agent architectures, multi-agent systems,
  • There are many definitions of agents often too
    narrow or too general.

Definitions of Agents 1
  • American Heritage Dictionary
  • ... One that acts or has the power or authority
    to act ... or represent another
  • Russel and Norvig
  • An agent is anything that can be viewed as
    perceiving its environment through sensors and
    acting upon that environment through effectors.
  • Maes, Pattie
  • Autonomous Agents are computational systems that
    inhabit some complex dynamic environment, sense
    and act autonomously in this environment, and by
    doing so realize a set of goals or tasks for
    which they are designed.

Definitions of Agents 2
  • IBM
  • 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 doing so, employ
    some knowledge or representations of the users
    goals or desires.

Definitions of Agents 3
  • An agent is autonomous capable of acting
    independently, exhibiting control over its
    internal state.
  • An agent is a computer system capable of
    autonomous action in some environment.

Definition of Agent 4
  • Examples of trivial/non-interesting agents are
  • Thermostat, UNIX deamon, e.g. biff
  • An intelligent agent is a computer system capable
    of flexible autonomous action in some
  • By flexible we mean
  • Reactive
  • Pro-active
  • Social

Properties of Agents 1
  • Autonomous
  • Capable of independent action without our
  • Reactive
  • Maintains an ongoing interaction with its
    environment and responds to changes that occur in
    (in time for the response to be useful).
  • Pro-active
  • Generating and attempting to achieve goals not
    driven solely by events taking the initiative.
  • Social
  • The ability to interact with other agents (and
    possibly humans) via some kind of agent
    communication language and perhaps cooperate with

Properties of Agents 2
  • Mentalistic notions, such as beliefs and
    intentions are often referred to as properties of
    strong agents.
  • Other properties are
  • Mobility the ability of an agent to move around
    a network.
  • Veracity agent will not knowingly communicate
    false information.
  • Benevolence agents do not have conflicting goals
    and always try to do what is asked of it.
  • Rationality an agent will act in order to
    achieve its goals and will not act in such a way
    as to prevent its goals being achieved.

Agents and Objects 1
  • Are agents just objects by another name?

Objects do it for free
  • Agents do it because they want to!
  • Agents do it for money!

Agents and Objects 2
  • Main differences
  • Agents are autonomous agents embody a stronger
    notion of autonomy than objects, in particular,
    agents decide for themselves whether or not to
    perform an action.
  • Agents are smart capable of flexible (reactive,
    pro-active social) behaviour standard object
    models do not have such behaviour.
  • Agents are active a multi-agent system is
    inherently multi-threaded in that each agent is
    assumed to have atleast one thread of active

Lets take a minute
  • Discuss with your neighbour what you think of
    this definition.
  • Try to come up with a few examples of agents that
    you know.

Why agents?
  • Today, we have a distributed environment that
    cannot be completely specified open
  • Former paradigms, such as OOP, cannot completely
    satisfy our needs
  • They were designed for constructing systems in a
    completely specified environment - a closed world.

How can we work in an Open Environment
  • By copying human behaviour
  • Perceive the environment
  • Affect the environment
  • Have a model of behaviour
  • Have intentions and motivations to be fulfilled
    by implementing corresponding goals

Distributed Artificial Intelligence (DAI)
  • DAI is a sub-field of AI
  • DAI is concerned with problem solving where
    agents solve (sub-) tasks (macro level)
  • Main areas of DAI
  • Multi-Agent Systems (MAS)
  • Distributed Problem Solving (DPS)

Reference B. Moulin, B. Chaib-draa. An Overview
of Distributed Artificial Intelligence. In G.
M. P. O'Hare, N. R. Jennings (eds). Foundations
of Distributed Artificial Intelligence, John
Wiley Sons, 1996, pp. 3-56.
DAI Concerns
  • DAI is concerned with
  • Agent granularity
  • Heterogenity of agents
  • Methods of distributing control among agents
  • Communication possibilities
  • DAI is not concerned with
  • Issues of coordination of concurrent processes at
    the problem solving and representational level
  • Parallel Computer Architectures, Parallel
    Programming Languages or Distributed Operating

  • DPS considers how the task of solving a
    particular problem can be divided among a number
    of modules that cooperate in dividing and sharing
    knowledge about the problem and its evolving
  • MAS is concerned with the behaviour of a
    collection of autonomous agents aiming to solve a
    given problem.

  • An important concept in DAI and MAS
  • No central control control is distributed
  • Knowledge or information sources may also be

Multi-agent Systems (MAS)
  • Contains a number of agents which interact with
    one another through communication. The agents are
    able to act in an environment where each agent
    will act upon or influence different parts of the
    environment. Reference Wooldridge, An
    Introduction to Multiagent Systems, p. 105

Motivation for MAS
  • To solve problems that are too large for a
    centralized agent
  • To allow interconnection and interoperation of
    multiple legacy systems
  • To provide a solution to inherently distributed
  • To provide solutions which draw from distributed
    information sources
  • To provide solutions where expertise is
  • To offer conceptual clarity and simplicity of

Benefits of MAS
  • Faster problem solving
  • Decrease in communication
  • Flexibility
  • Increased reliability

Cooperative and Self-interested MAS
  • Cooperative
  • Agents designed by interdependent designers
  • Agents act for increased good of the system
  • Concerned with increasing the performance of the
  • Self-interested
  • Agents designed by independent designers
  • Agents have their own agenda and motivation
  • Concerned with the benefit and performance of the
    individual agent
  • More realistic in an Internet setting?

Interaction and Communication in MAS
  • To successfully interact, agents need ability to
    cooperate, coordinate and negotiate.
  • This requires communication
  • Plan /message passing
  • Information exchange using shared repositories
  • Important characteristics of communication
  • Relevance of the information
  • Timeliness
  • Completeness

Lets take a minute
  • Discuss with your neighbour
  • A problem that can be solved by a MAS
  • Advantages and disadvantages of using a MAS for
    your particular problem

Agent Typology 1
  • One of the most referred to typologies is given
    by Nwana, BT Research Labs
  • Reference H. S. Nwana. Software Agents An
    Overview, Knowledge Engineering Review, Vol. 11,
    No. 3, 1996, 40 pages
  • Several dimensions of typology
  • Mobility - mobile or static.
  • Deliberative or reactive.
  • Primary attributes, such as autonomy, learning
    and cooperation.

Agent Typology 2
  • A part view of an agent typology

Autonomous Software systems
Smart Agents
Collaborative Agents
Interface Agents
Agent Typology 3
Nwana identified the following seven types of
  • Collaborative agents - autonomous and cooperate.
  • Interface agents - autonomous and learn.
  • Mobile agents - able to move around a network.
  • Information/Internet agents - manages the
    information on the internet.
  • Reactive agents - stimulus-response behaviour.
  • Hybrid agents - combination two or more agent
  • Smart agents - autonomous, learn and cooperate.
  • Criticisms of this Typology
  • Confuses agents with what they do (e.g. Info
    search) and the technology (e.g. reactive,

Agent Typology 4
  • Collaborative agents
  • Hypothesis/Goal The capabilities of the
    collection of agents is greater than any of its
  • Main Motivation To solve problems that are too
    large for a single agent.
  • Interface agents
  • Hypothesis/Goal A personal assistant that
    collaborates with the user.
  • Main Motivation To eliminate humans performing
    several, manual sub-operatioions.
  • Example A personal assistant that finds a
    suitable package holiday for the user.

Agent Typology 5
  • Mobile agents
  • Hypothesis/Goal Agents need not be stationary!
  • Main Motivation To reduce communication costs
  • Example Aglets
  • Information/Internet agents
  • Hypothesis/Goal Reduce information overload
  • Main Motivation The need for tools to manage
    information explosion
  • Example agents that reside on servers and access
    the distributed on-line information on the

Agent Typology 6
  • Reactive agents
  • Hypothesis/Goal Physical grounding hypothesis
    representations grounded in the physical world.
  • Hybrid agents
  • Defnconstitutes a combination of two or more
    agent philosophies (e.g deliberative reactive).
  • Hypothesis/Goal Gains from the combination of
    philosophies gtgt gains from the same philosophy.

Agent Typology 7
  • Heterogeneous agents
  • Definition System of agents of several types
  • e.g. mobile and interface agents in the same
  • Realistic in an Internet (open-system) setting
  • Motivation Interoperability is plausible
  • Requires Standards for communication among the
  • Agent Communication Languages and protocols
  • Cooperation conventions

Other Types of Agents
Some of these may not exhibit any agent
properties as discussed earlier.
  • Desktop Agents e.g.
  • Operating System agents interact with the OS to
    perform tasks on behalf of the user.
  • Application agents e.g. Wizards
  • Web search agents act as information brokers
    between information suppliers (e.g. Websites) and
    information consumers (e.g. users)

Operating System Agents

GUI Shell
Memory Mgmt.
File Mgmt.
Process Mgmt.
Web Search Agents

Query Server
Web Browser
Index database
Web robot
Search Engine
Information Filtering Agents

News Server
Web Browser
Indexed articles
User Profile
Indexing Engine
Filtering Agent
Lets take a minute
  • Discuss with your neighbour the main points in
    this lecture.

  • An agent is a system that is capable of
    independent action on behalf of its user or
  • A multi-agent system is one that consists of a
    number of agents which interact with one another.
  • In order to successfully interact, agents need
    ability to cooperate, coordinate and negotiate.

Definition of Agents - Summary
  • An agent acts on behalf of another user or entity
  • An agent has the weak agent properties
  • autonomy, pro-activity, reactivity and social
  • An agent may have strong agent properties
  • mentalistic notions such as beliefs and desires
  • Other properties discussed in the context of
  • mobility, veracity, benevolence and rationality

  • Curriculum Wooldridge Introduction to MAS
  • Chapters 1 2
  • ArticleAgent Typology
  • H. S. Nwana. Software Agents An Overview,
    Knowledge Engineering Review, Vol. 11, No. 3,
    1996, 40 pages
  • Recommended Reading (not curriculum)
  • B. Moulin, B. Chaib-draa. An Overview of
    Distributed Artificial Intelligence. In G. M.
    P. O'Hare, N. R. Jennings (eds). Foundations of
    Distributed Artificial Intelligence, John Wiley
    Sons, 1996, pp. 3-56.

Next Lecture Multi-agent Interactions
  • Will be based on
  • Multi-agent Interactions, Chapter 6 in
    Wooldridge Introduction to MultiAgent Systems

FBI Agents Ordering Pizza
FBI agents conducted a raid of a psychiatric
hospital in San Diego that was under
investigation for medical insurance fraud. After
hours of reviewing thousands of medical records,
the dozens of agents had worked up quite an
appetite. The agent in charge of the
investigation called a nearby pizza parlour with
delivery service to order a quick dinner for his
colleagues. The following telephone conversation
took place and was recorded by the FBI because
they were taping all conversations at the
hospital. Source http//
FBI Agents Ordering Pizza, contd.
  • Agent Hello. I would like to order 19 large
    pizzas and 67 cans of soda.
  • Pizza Man And where would you like them
  • Agent We're over at the psychiatric hospital.
  • Pizza Man The psychiatric hospital?
  • Agent That's right. I'm an FBI agent.
  • Pizza Man You're an FBI agent?
  • Agent That's correct. Just about everybody here
  • Pizza Man And you're at the psychiatric
  • Agent That's correct. And make sure you don't
    go through the front doors. We have them locked.
    You will have to go around to the back to the
    service entrance to deliver the pizzas.
  • Pizza Man And you say you're all FBI agents?
  • Agent That's right. How soon can you have them
  • Pizza Man And everyone at the psychiatric
    hospital is an FBI agent?
  • Agent That's right. We've been here all day and
    we're starving.
  • Pizza Man How are you going to pay for all of
  • Agent I have my checkbook right here.
  • Pizza Man And you're all FBI agents?
  • Agent That's right. Everyone here is an FBI
    agent. Can you remember to bring the pizzas and
    sodas to the service entrance in the rear? We
    have the front doors locked.
  • Pizza Man I don't think so. Click.