Introduction Lecture 01

7/29/2019 Introduction Lecture 01

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LECTURE 1: INTRODUCTIONLECTURE 1: INTRODUCTION

Multiagent Systems

Based on An Introduction to MultiAgent

Systems by Michael Wooldridge, John Wiley &Sons, 2002.http://www.csc.liv.ac.uk/mjw/pubs/imas/

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OverviewOverview

Five ongoing trends have marked the history

of computing:ubiquity;

intelligence;

delegation; and

human-orientation

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UbiquityUbiquity

The continual reduction in cost of computing

capability has made it possible to introduceprocessing power into places and devices that would

have once been uneconomic

What could benefit from having a processor

embedded in it?

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InterconnectionInterconnection 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

Distributed and concurrent systems have become the

norm.

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IntelligenceIntelligence The complexity of tasks that we are capable of

automating and delegating to computers hasgrown steadily

If you dont feel comfortable with this

definition of intelligence, its probably

because you are a human

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DelegationDelegation Computers are doing more for us without our

intervention We are giving controlto computers, even in safety

critical tasks

One example: fly-by-wire aircraft, where the machinesjudgment may be trusted more than an experienced

pilot

Next on the agenda: fly-by-wire cars, intelligent brakingsystems, cruise control that maintains distance from

car in front

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Human OrientationHuman Orientation The movement away from machine-oriented views

of programming toward concepts and metaphorsthat more closely reflect the way we ourselves

understand the world

Programmers an users re ate to t e mac nedifferently

Programmers conceptualize and implement software

in terms of higher-level more human-oriented abstractions

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Programming progressionProgramming progression Programming has progressed through:

machine code;

assembly language;

-

sub-routines;

procedures & functions;

abstract data types;

objects;

to agents.8


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Global ComputingGlobal Computing What techniques might be needed to deal

with systems composed of 1010 processors? Dont be deterred by its seeming to be

Hundreds of millions of people connected by

email once seemed to be science fiction

Lets assume that current softwaredevelopment models cant handle this

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Where does it bring us?Where does it bring us? Delegation and Intelligence imply the need to build

computer systems that can act effectively on ourbehalf

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 otherhumans or systems

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Interconnection and DistributionInterconnection 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,

mp es systems t at can cooperate an reacagreements (or even compete) with other systems

that have different interests (much as we do with

other people)

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So Computer Science expandsSo Computer Science expands These issues were not studied in Computer

Science until recently All of these trends have led to the emergence

multiagent systems

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Agents, a DefinitionAgents, a Definition An agent is a computer system that is

capable ofindependentaction on behalf ofits user or owner (figuring out what needs to

be done to satisfy design objectives, rather

than constantly being told)

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Multiagent Systems, a DefinitionMultiagent Systems, a Definition A multiagent system is one that consists of a

number of agents, which interactwith one-another

In the most general case, agents will be acting on

behalf of users with different goals andmotivations

To successfully interact, they will require the

ability to cooperate, coordinate, and negotiatewith each other, much as people do

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Agent Design, Society DesignAgent 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?

agent design, and society design (micro/macro)

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Multiagent SystemsMultiagent Systems In MAS, 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, andhow can they (nevertheless) reach agreement?

How can autonomous agents coordinate their activities so

as to cooperatively achieve goals?

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Multiagent SystemsMultiagent Systems

While these questions are all addressed in

part by other disciplines (notablyeconomics and social sciences), what

emphasizes that the agents in question arecomputational, information processing

entities.

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The Vision ThingThe Vision Thing Its easiest to understand the field of MAS 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 toconsider each others work relevant to their own

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Spacecraft ControlSpacecraft 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

, .

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!

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Deep Space 1Deep Space 1 http://nmp.jpl.nasa.gov/ds1/

Deep Space 1launched from CapeCanaveral on October 24,1998. During a highly

,

it tested 12 advanced, high-risk technologies in space. Inan extremely successful extended mission, it encounteredcomet Borrelly and returned the best images and otherscience data ever from a comet. During its fully successfulhyperextended mission, it conducted further technologytests. The spacecraft was retired on December 18, 2001.

NASA Web site

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Autonomous Agents for specialized tasksAutonomous 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.

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Air Traffic ControlAir Traffic Control A key air-traffic control systemsuddenly 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

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Internet AgentsInternet Agents After the wettest and coldest UK winter on record,

you are in desperate need of a last minute holidaysomewhere warm and dry. After specifying your

requirements to your personal digital assistant

, converses w a num er o eren e

sites, which sell services such as flights, hotel rooms,

and hire cars. After hard negotiation on your behalf

with a range of sites, your PDA presents you with

package holiday.

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Research IssuesResearch 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 gooddeal)?

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MAS is InterdisciplinaryMAS is Interdisciplinary The field of MAS is influenced and inspired by many

other fields:

Economics

osop y

Game Theory

Logic

Ecology

Social Sciences

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Some Views of the FieldSome 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 widel reco nized that interaction is robabl 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 whichinteraction is the norm

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Some Views of the FieldSome Views of the Field Agents as a tool for understanding human

societies:

Multiagent systems provide a novel new tool fors mu at ng soc et es, w c may e p s e somelight on various kinds of social processes.

This has analogies with the interest in theories of

the mind explored by some artificial intelligenceresearchers

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Objections to MASObjections to MAS Isnt it all just Distributed/Concurrent

Systems?

Agents are assumed to be autonomous, capableo ma ng n epen ent ec s on so t ey neemechanisms to synchronize and coordinate theiractivities at run time

In a MAS each agent is (can be) self-interested,oppose to distributed/concurrent systems.

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Objections to MASObjections to MAS Isnt it all just AI?

We dont need to solve all the problems ofartificial intelligence (i.e., all the components of

intelligence) in order to build really useful agents.

Intelligent agents are 99% computer science and1% IA Oren Etzioni.

Classical AI ignored socialaspects of agency. Theseare important parts of intelligent activity in real-

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Objections to MASObjections to MAS Isnt it all just Economics/Game Theory?

These fields also have a lot to teach us in multiagentsystems, but:

Insofar as game theory provides descriptive

concepts, it doesnt always tell us howtocompute solutions; were concerned with

computational, resource-bounded agents

Some assumptions in economics/game theory(such as a rational agent) may not be valid or

useful in building artificial agents

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Objections to MASObjections to MAS Isnt it all just Social Science?

We can draw insights from the study of humansocieties, but there is no particular reason to

believe that artificial societies will be constructed

in the same way

Again, we have inspiration and cross-fertilization,

but hardly subsumption

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What is an Agent?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

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SYSTEM

ENVIRONMENT

input output


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What is an Agent?What is an Agent?

SYSTEM

ENVIRONMENT

input output

The key problem facing an agent is that of deciding which

of its actions it should perform in order to best satisfy its

design objectives.

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What is an Agent?What is an Agent? Trivial (non-interesting) agents:

thermostat

UNIX daemon (e.g., biff)

An intelligent agent is a computer system capable of

ex e au onomous ac on n some env ronmen

Byflexible, we mean:

reactive

pro-active

social

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ReactivityReactivity 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

, .

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

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ProactivenessProactiveness 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 toachieve goals; not driven solely by events; taking

the initiative

Recognizing opportunities

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Balancing Reactive and GoalBalancing Reactive and Goal--OrientedOriented

BehaviorBehavior

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 oneanother

Designing an agent that can balance the two remains

an open research problem

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Social AbilitySocial 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 theInternet

Social abilityin agents is the ability to interact with other

agents (and possibly humans) via some kind ofagent-

communication language, and perhaps cooperate with

others

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Other PropertiesOther 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

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EnvironmentsEnvironments AccessibleAccessible vs.vs. inaccessibleinaccessible

An accessible environment is one in which the

agent can obtain complete, accurate, up-to-dateinformation about the environments state

The more accessible an environment is, the simplerit is to build agents to operate in it

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EnvironmentsEnvironments

DeterministicDeterministic vs.vs. nonnon--deterministicdeterministic

A deterministic environment is one in which any

action has a single guaranteed effect there is no

uncertainty about the state that will result from

performing an action

Non-deterministic environments present greater

problems for the agent designer

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EnvironmentsEnvironments -- StaticStatic vs.vs. dynamicdynamic A static environment is one that can be assumed to remain

unchanged except by the performance of actions by the agent

A dynamic environment is one that has other processes

operating on it, and which hence changes in ways beyond the

agents control

Other processes can interfere with the agents actions (as in

concurrent systems theory)

The physical world is a highly dynamic environment

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Agents and ObjectsAgents and Objects Are agents just objects by another name?

Object:encapsulates some state

has methods, corresponding to operations

that may be performed on this state

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Agents and ObjectsAgents 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

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Objects do it for freeObjects do it for free agents do it because they want to

agents do it for money

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Agents and Expert SystemsAgents 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)

xamp e: nows a out oo seases n

humans

It has a wealth of knowledge about blood diseases, in the

form of rules

A doctor can obtain expert advice about blood diseases bygiving MYCIN facts, answering questions, and posing queries

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Agents and Expert SystemsAgents 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

questionsagents act:

MYCIN does not operate on patients

Some real-time (typically process control)expert systems are agents

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Introduction Lecture 01

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