Introduction-Multiagent Systems-Lecture Slides, Slides for Multiagent Systems. Aliah University

Multiagent Systems

Description: Prof. Balkishan Sachin delivered this lecture at Aliah University for Multiagent Systems course. Its main points are: Introduction, Trends, Ubiquity, Interconnection, Delegation, Intelligence, Human-orientation, Programming, Progression
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LECTURE 1: INTRODUCTION

LECTURE 1:

INTRODUCTION

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Overview

 Five ongoing trends have marked the history

of computing:

ubiquity;

interconnection;

intelligence;

delegation; and

human-orientation

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Ubiquity

 The continual reduction in cost of computing

capability has made it possible to introduce

processing power into places and devices

that would have once been uneconomic

 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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Interconnection  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…

 Since distributed and concurrent systems

have become the norm, some researchers

are putting forward theoretical models that

portray computing as primarily a process of

interaction

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Intelligence

 The complexity of tasks that we are capable

of automating and delegating to computers

has grown steadily

 If you don’t feel comfortable with this

definition of ―intelligence‖, it’s probably

because you are a human

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Delegation

 Computers are doing more for us – without

our intervention

 We are giving control to computers, even in

safety critical tasks

 One example: fly-by-wire aircraft, where the

machine’s 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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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 the

machine differently

 Programmers conceptualize and implement

software in terms of higher-level – more

human-oriented – abstractions

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Programming progression…

 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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Global Computing

 What techniques might be needed to deal

with systems composed of 1010 processors?

 Don’t be deterred by its seeming to be

―science fiction‖

 Hundreds of millions of people connected by

email once seemed to be ―science fiction‖…

 Let’s assume that current software

development models can’t handle this…

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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 act

independently

 The ability of computer systems to act in a way

that represents our best interests while interacting

with 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 can

cooperate and reach agreements (or even

compete) with other systems that have

different interests (much as we do with other

people)

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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

capable of independent action on behalf of

its 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 Definition

 A multiagent system is one that consists

of a number of agents, which interact with

one-another

 In the most general case, agents will be

acting on behalf of users with different

goals and motivations

 To successfully interact, they will require

the ability to cooperate, coordinate, and

negotiate with each other, much as

people do

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Agent 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?

 The first problem is agent design, the second is

society design (micro/macro)

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Multiagent Systems

 In Multiagent Systems, 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,

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), what

makes the multiagent systems field

unique is that it emphasizes that the

agents in question are computational,

information processing entities.

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The Vision Thing

 It’s easiest to understand the field of multiagent

systems 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 field’s researchers clearly have enough

in common to consider each other’s work

relevant to their own

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Spacecraft 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 required quickly, it is

simply not practicable. For these 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: NASA’s DS1 has done it!

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

 ―Deep Space 1 launched from Cape Canaveral on October 24, 1998. During a highly successful primary mission, it tested 12 advanced, high-risk technologies in space. In an extremely successful extended mission, it encountered comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, 2001.‖ – NASA Web site

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Autonomous 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 (remote human control may

be an alternative, but not always)

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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 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 Agents

 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 pieces of information from

various different Internet information sources.

Failure would occur when a particular resource

was unavailable, (perhaps due to network

failure), or where results could not be obtained.

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What if the agents become better?

 Internet agents need not simply search

 They can plan, arrange, buy, negotiate –

carry out arrangements of all sorts that would

normally be done by their human user

 As more can be done electronically, software

agents theoretically have more access to

systems that affect the real-world

 But new research problems arise just as

quickly…

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Research 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 good deal)?

 These issues aren’t frivolous – automated procurement could be used massively by (for example) government agencies

 The Trading Agents Competition…

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Multiagent Systems is Interdisciplinary

 The field of Multiagent Systems is influenced and

inspired 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 a progressively

better understanding of the characteristics of

complexity in software. It is now widely

recognized that interaction is probably 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 which

interaction is the norm

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Some Views of the Field

Agents as a tool for understanding human

societies:

Multiagent systems provide a novel new

tool 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 some

artificial intelligence researchers

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Some Views of the Field

Multiagent Systems is primarily a search for

appropriate theoretical foundations:

We want to build systems of interacting,

autonomous agents, but we don’t yet know

what these systems should look like

 You can take a ―neat‖ or ―scruffy‖ approach to

the 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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Objections to MAS

 Isn’t 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 their activities at run time

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

interactions are ―economic‖ encounters

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Objections to MAS

 Isn’t it all just AI?

 We don’t need to solve all the problems of

artificial intelligence (i.e., all the components

of intelligence) in order to build really useful

agents

 Classical AI ignored social aspects of

agency. These are important parts of

intelligent activity in real-world settings

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Objections to MAS

 Isn’t 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 doesn’t always tell us how to

compute solutions; we’re 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 MAS

 Isn’t it all just Social Science?

 We can draw insights from the study of

human societies, 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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