lect01 introtoagents10 100201051415 phpapp01

7/26/2019 Lect01 Introtoagents10 100201051415 Phpapp01

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LECTURE 1:

INTRODUCTION TO

AGENTS AND MAS

Artificial Intelligence II Multi-Agent Systems

Introduction to Multi-Agent Systems

URV, Winter-Spring 2010


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Outline of the lecture

Main trends in Computer Science

Agents and multi-agent systems Viewpoints on agent technology

Agent technology roadmap Challenges on agent technology

Objections to MAS


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Important aspects in Computer Science

Five ongoing trends are marking the history

of computing:

ubiquity

interconnection

intelligence delegation

human-orientation in programming methodologies


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Ubiquity

The continuous reduction in cost of

computing capability has made it possible to

introduce processing power into places anddevices that would have once been

uneconomic / unimaginable [e.g. Nike+]

As processing capability spreads,

sophistication (and intelligence of a sort)

becomes ubiquitous What could benefit from having a processor

embedded in it?


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Ambient Intelligence

Aimed at seamless delivery of services and applications.

Relies on ubiquitous computing, ubiquitouscommunication and intelligent user interfaces.

The vision An environment of potentially thousands of embedded and

mobile devices (or software components) interacting to supportuser-centred goals and activity.

Suggests a component-oriented view of the world in which thecomponents are independent and distributed.

Autonomy, distribution, adaptation, responsiveness, and so on,are key characteristics of these components, and in this sense

they share the same characteristics as agents.

Requires agents to be able to interact with numerousother agents in the environment around them in order to

achieve their goals.


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Interconnection

Computer systems today no longer stand

alone, but are networked into large

distributed systems Obvious example: Internet

Since distributed and concurrent systemshave become the norm, some researchers

are putting forward theoretical models that

portray computing as primarily a process ofinteraction


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Connectivity -Technological context

Semantic Web

The Semantic Web is based on the idea that the data

on the Web can be defined and linked in such a waythat it can be used by machines for the automatic

processing and integration of data across different

applications (Berners-Lee et al., 2001).

Peer-to-Peer computing

Grid computing


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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 probablybecause you are a human


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Delegation

Computers are doing more for us withoutour intervention

We are giving control to computers, even insafety 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


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Driving automation

1. Road ConditionReporting2. Adaptive Cruise

Control3. OmnidirectionalCollision System

4. Lane-DeparturePrevention5. Auto Parallel Park

6. Blind-SpotSensors7. Corner Speed


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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 themachine differently

Programmers conceptualize and implement

software in terms of higher-level more

human-oriented abstractions


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Agent-Oriented Software Engineering

Programming has progressed through

machine code

assembly language

machine-independent programming languages

sub-routines procedures & functions

abstract data types

objects

to agents.


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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 actindependently

The ability of computer systems to act in a way

that represents our best interests while interactingwith other humans or systems


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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 cancooperate and reach agreements (or even

compete) with other systems that have

different interests (much as we do with otherpeople)


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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


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Agents, a Definition

An agent is a computer system that is located in adynamic environment and is capable ofindependent action on behalf of its user or owner(figuring out what needs to be done to satisfydesign objectives, rather than constantly beingtold)

Intelligent action, probably using ArtificialIntelligence tools and techniques

An agent should satisfy a certain number of

properties [lecture on week 3]


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Multiagent

Systems, a Definition A multiagent system is one that

consists of a number of agents,which interact with one-another

In the most simple case, allagents are programmed by thesame team and they collaborateto complete a task

In the most general case,agents will be acting on behalfof users with different goals andmotivations

To successfully interact, theywill require the ability tocooperate, coordinate, andnegotiate with each other, much

as people do


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Related areas

Several sub-disciplines of informationtechnology are related to software agenttechnology:

computer networks,

software engineering,

Artificial Intelligence, human-computer interaction,

distributed and concurrent systems,

mobile systems, computer-supportedcooperative work, control systems, decisionsupport, information retrieval and management,electronic commerce...


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Micro and macro aspects

Agent technologies can be grouped into threecategories Agent-level [micro level]

technologies and techniques concerned only with individualagents for example, procedures for agent reasoning andlearning.

Interaction-level

technologies and techniques that concern thecommunications between agents

communication languages, interaction protocols andresource allocation mechanisms.

Organization-level [macro level]

technologies and techniques related to agent societies as awhole.

structure, trust, norms, obligations, etc.


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Multiagent

Systems

In Multiagent Systems, we address questions

such as:

What kinds of languages can agents use tocommunicate?

How can cooperation emerge in societies of self-

interested agents? 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?


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Multiagent

Systems

While these questions are all addressed

in part by other disciplines (notably

Economics and Social Sciences), whatmakes the multiagent systems field

unique is that it emphasizes that theagents in question are computational,

information processing entities.


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Example 1 -

Spacecraft

Control

When a space probe makes a flight 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. NASA is investigating the possibility ofmaking probes more autonomous giving them

richer decision making capabilities and

responsibilities. This is not fiction: NASAs DS1 has done it!


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Autonomous Agents for specialized tasks

The DS1 example is one of a generic class

Agents (and their physical instantiation inrobots) 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 maybe an alternative, but not always)


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Example 2 -

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 allaffected flights.

Systems taking the initiative when necessary Agents cooperating to solve problems beyond

the capabilities of any individual agent


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Example 3 -

Internet

Agents

Searching the Internet for the answer to a

specific query can be a long and tedious

process. So, why not allow a computerprogram 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.


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Example 4

e-personal assistants

Internet agents need not simply search

They can plan, arrange, buy, negotiate

carry out arrangements of all sorts that wouldnormally be done by their human user

As more can be done electronically, softwareagents theoretically have more access to

systems that affect the real-world

For example, preparing a holiday trip


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Research Issues

How do you state your preferences to youragent?

How can your agent compare different dealsfrom different vendors? What if there are manydifferent parameters?

What algorithms can your agent use tonegotiate with other agents?

These issues arent frivolous automatedprocurement could be used massively by (forexample) government agencies


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Multiagent

Systems is Interdisciplinary

The field of Multiagent Systems is influenced andinspired 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


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Some Views of the Field

Agents as a paradigm for software

engineering:

Software engineers have derived aprogressively better understanding of the

characteristics of complexity in software. It

is now widely recognized that interaction isprobably the most important single

characteristic of complex software


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Agents as design metaphor

Agent-oriented software engineering

Agents allow software designers and developersto structure an application using autonomous,communicative components.

In this sense, software agents offer a new and

often more appropriate route to the developmentof complex computational systems, especially inopen and dynamic environments.

Modularity, reusability,collaborative behaviour


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Agents as source of technologies

Software agent technologies span a range ofspecific techniques and algorithms

balancing reaction and deliberation in individualagent architectures [lecture 2]

learning from and about other agents in the

environment eliciting and acting upon user preferences

finding ways to negotiate and cooperate with

other agents developing appropriate means of forming and

managing coalitions (and other organizations)


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Social simulation

Agents as a tool for understanding human

societies:

Multiagent systems provide a novel newtool 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 someArtificial Intelligence researchers


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Agent-based simulation

Multi-agent systems provide strong models

for representing complex and dynamic real-

world environments. modelling of the impact of climate change on

biological populations

modelling the impact of public policy options onsocial or economic behaviour

modelling intelligent buildings

modelling traffic systems

modelling biological populations


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Distributed computing theory

Multiagent Systems is primarily a search for

appropriate theoretical foundations:

We want to build systems of interacting,autonomous agents, but we dont yet know

what these systems should look like

You can take a neat or scruffy approach tothe problem, seeing it as a problem of theory

or a problem of engineering This, too, has analogies with Artificial

Intelligence research


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Agent technology roadmap [AgentLink]

There are four broad phases Phase 1: current

Phase 2: short-term future

Phase 3: medium-term future

Phase 4: long-term future

Time phases distinguished along dimensions: The degree to which the participating agents share

common domain knowledge and common goals.

The degree to which participating agents aredesigned by the same or diverse design teams.


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Phase 1: present state

Systems are designed by the same teams.

Systems share common domain knowledge.

Participating agents have a global objective.

Agent communication languages are based onstandards.

Interaction protocols remain non-standard.

Low scalability, few agents.

Example systems include those to enable

automated scheduling coordination betweendifferent departments, etc.


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Phase 2: short-term future

Multi-agent systems will increasingly be designed to crosscorporate boundaries.

Participating agents have few goals in common, althoughtheir interactions will still concern a common domain

Agents will be designed by a same team, and will sharecommon domain knowledge.

Standard agent communication languages, such as FIPA

ACL, will be used. Interaction protocols will be mixed between standard and

non-standard ones.

Systems will be able to handle large numbers of agents inpre-determined environments.

Development methodologies, languages and tools will havereached a degree of maturity.


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Phase 3: medium-term future

Multi-agent systems will permit participation byheterogeneous agents, designed by different designers orteams.

Any agent will be able to participate in these systems.

Their (observable) behaviour conforms to publicly-stated requirements and standards.

Open systems will typically be specific to particular

application domains, such as B2B eCommerce orbioinformatics.

The languages and protocols used will be agreed andstandardised, perhaps drawn from public libraries of

alternative protocols. Commonly agreed modelling languages

Agent-UML


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Phase 4: Long-term future

Truly open and fully-scalable agent systemsspanning multiple application domains and

developed by diverse design terms Agents learn appropriate protocols and behaviour

upon entry to system

Languages, protocols, and behaviours emergefrom actual agent interactions.

Evolving organisational structure with multiple,dynamic, interacting organisations.

Self-modifying agent communications languages.

Use of agent-specific design methodologies.


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Challenges in agent technology

Implementing multi-agent systems is still a complextask. Lack of maturity in both methodologies and programming

tools.

Lack of specialized debugging tools.

Lack of skills needed to move from analysis and design tocode.

Problems associated with awareness of the specifics ofdifferent agent platforms.

Problems in understanding the nature of what is a new anddistinct approach to systems development.


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Technological Challenges

Increase quality of agent software to industrialstandard

Agent oriented design methodologies, tools anddevelopment environments.

Seamless integration with existing technologies.

Web, information systems

Non-functional properties: scalability, performance,reliability, and robustness.

Analysis point of view

Agent technology requires dedicated basicconcepts and languages: Dynamic aspects, such as time and action.

Locality aspects, such as position in a space.


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Increase quality of agent software to industrial

standard (cont.)

Design point of view The key point is reusability

Libraries of

Generic organisation models (hierarchical, flat, etc.).

Generic agent models (reactive, deliberative, etc.)

Generic task models (diagnostic, information filtering, etc.).

Communication languages and patterns for agent societes.

Ontology patterns for agent requirements, models, and organisation.

Interaction protocol patterns between agents with special roles. Reusable knowledge bases.


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Develop reasoning capabilities for agents

Virtual organisations, e-institutions

Heterogeneity of agents, trust, and failurehandling and recovery.

Norms, legislation, authorities, and enforcement.

Coalition formation Dynamic coalitions of small groups of agents.

Negotiation and argumentation strategies

Distributed planning techniques


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Develop agent ability to understand userrequirements

User profiling Personalisation

Knowledge acquisition tools

Develop agent ability to adapt to changesin environment

Learning

Evolutionary programming techniques


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Ensure user confidence and trust in agents

Security technologies. Security in open agent systems.

Agents must be secure and must not reveal informationinappropriately.

Confidence that agents representing other parties willbehave within certain constrains.


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Ensure user confidence and trust in agents(cont).

Models and infrastructure for trust and reputation. Reputation mechanisms to assess the past behaviour of

particular agents or users, to allow avoidance ofuntrustworthy agents.

The adoption of norms (social rules) by all members ofan open agent system.

Enforcement of sanctions against agents not followingrules (or self-enforcing protocols).

The use of electronic contracts to represent and enforceagreements between several parties.


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Technological Challenges Provide semantic infrastructure for open agentcommunities

Ontologies

Semantic Web

Matchmaking and broker architectures

Exiting out of laboratories requires A greater understanding of how agents, databases and

information systems interact. Investigation of the real-world implications of information

agents.

Development of benchmarks for agent system performance

and efficiency. New web standards that enable structural and semantic

description of information.

Creation of common ontologies, thesauri, or knowledge

bases.


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Preconditions for wide-scale usage

Software agent technology is better than other ones.

At least, when compared using one measurable

parameter in the application field ....

There are enough trained, skillful persons to

develop applications with software agent

technology.

Development tools are easy to use and powerful

enough to develop applications.

Technology is generally known.

Including top-level management.


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Objections to MAS (I)

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 theiractivities at run time

Agents are (can be) self-interested, so their

interactions are economic encounters(negotiation)


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Objections to MAS (II)

Isnt it all just AI?

We dont need to solve all the problems of

Artificial Intelligence (i.e., all the components ofintelligence) in order to build really useful agents

Classical AI ignored social aspects of agency.

These are important parts of intelligent activity inreal-world settings


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Objections to MAS (III)

Isnt it all just Economics/Game Theory?These fields also have a lot to teach us in

multiagent systems, but:

Insofar as game theory provides descriptive

concepts, it doesnt always tell us how to compute

solutions; were concerned with computational,resource-bounded agents

Some assumptions in economics/game theory

(such as a rational agent) may not be valid oruseful in building artificial agents

b A


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Objections to MAS (IV)

Isnt it all just Social Science?

We can draw insights from the study of

human societies, but there is no particularreason to believe that artificial societies will be

constructed in the same way

Again, we have inspiration and cross-fertilization, but hardly subsumption

R di f hi k


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Readings for this week

M.Wooldridge:An

introduction to MultiAgent

Systems chapter 1 A.Mas:Agentes software y

sistemas multi-agente:

conceptos, arquitecturas yaplicaciones initial part of

chapter 1

Agent Technology:

Computing as Interaction

AgentLink III roadmap

Mi d h 1 (W ld id )


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Mindmap, chapter 1 (Wooldridge)

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