Introduction to Agents and Multi-agent Systems (lecture slides)

D. Monett – Europe Week 2014, University of Hertfordshire, Hatfield
Introduction to Agents and
Multi-agent systems
Prof. Dr. Dagmar Monett Díaz
Computer Science Dept.
Faculty of Cooperative Studies
Berlin School of Economics and Law
dagmar@monettdiaz.com
Europe Week, 3rd – 7th March 2014

D. Monett – Europe Week 2014, University of Hertfordshire, Hatfield 2
Two “intelligent” agents…

Dilbert
Scott Adams
At http://dilbert.com/strips/comic/1991-02-08/
(Educational/Classroom usage permission is granted by Universal Uclick. All Rights Reserved)
“Intelligent” travel agent #1
3

Dilbert
Scott Adams
“Intelligent” travel agent #2
4

Another kind of “intelligent” agent

Travel agent: simple case
"Stimulus-response„ search engine
Customer: Agent:
Specifies request
with preferences
[Fill out form]
Specifies answer
[Show matching
offers]
6

Travel agent: more complex
Customer: Agent:
I want to go on
vacation
Wonderful!
Do you like
swimming?
7

Customer: Agent:
Yes, with good
friends on a white
beach.
And I like sports.
Wonderful!
And in the evening?
8

Customer: Agent:
Good entertainment,
exclusive bars, etc.
Sounds fantastic.
Is this what you are
looking for?
[Present an offer]
9

Customer: Agent:
Really fantastic
but over my budget.
I‘d prefer something
less exclusive...
Let's see…
How about this?
[Present a new offer]
10

What would that agent need for the
dialog?

12
Such a travel agent would need its knowledge to be
dynamic:

13
dynamic:
 Dialog history

14
dynamic:
 Dialog history
 (Hypothetical) model of the customer’s and own
• desires, intentions
• preferences, opinions

15
dynamic:
 Dialog history
 (Hypothetical) model of the customer’s and own
• desires, intentions
• preferences, opinions
 (Flexible) plan for
• exploring the customer’s desires and intentions
• profitable offers

Agenda

Agenda
 Where does the major content come from?
 What is an agent? What is a multi-agent system?
 Agent types
 Agent properties
 Design of intelligent agents
 Implementing practical reasoning agents
 Further reading, sources of inspiration, and more…

©

An Introduction to
MultiAgent Systems
Michael Wooldridge
2nd Edition, 484 pp.
John Wiley & Sons, May 2009
ISBN-13: 978-0470519462
With materials available at
http://www.csc.liv.ac.uk/~mjw/pub
s/imas/IMAS2e.html
What I also use in my
lectures at the HWR…
19

Artificial Intelligence: A
Modern Approach
Stuart Russell and Peter Norvig
3rd Edition, 1152 pp.
Prentice Hall, December 2009
ISBN-13: 978-0136042594
With materials available at
http://aima.cs.berkeley.edu/
What I also use in my
lectures at the HWR…
20
“The AI Bible”

What is an agent?

Agents, a definition
22
According to Michael Wooldridge:
“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)”.

23
“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”.

24
According to Russell and Norvig:
“An agent is anything that can be viewed as
perceiving its environment through sensors
and acting upon that environment through
actuators”.

Agents
Agents communicate with the environment
through Sensors and Actuators

What is a multi-agent system?

Dilbert
Scott Adams
Agent in a group
27

Multi-agent systems, a
definition
28
“A multi-agent system is one that consists of
a number of agents, which interact with one-
another”.

Multi-agent systems, a
definition
29
“A multi-agent 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

Agent types

Agent types
Four basic types in order of increasing
generality:
 Simple reflex agents
 Model-based reflex agents
 Goal-based agents
 Utility-based agents
31

Simple reflex agents

Model-based reflex agents

Goal-based agents

Utility-based agents

Learning agents

Dilbert
Scott Adams
A real state agent
37

Agent properties

Intelligent agents are…
 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
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 response to be useful).
39

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
Pro-activeness = generating and
attempting to achieve goals; not
driven solely by events; taking the
initiative; recognizing opportunities.
40

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
Social ability in agents is the ability
to interact with other agents (and
possibly humans) via some kind of
agent-communication language, and
perhaps cooperate with others.
41

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
An agent should strive to do the
right thing, based on what it can
perceive and the actions it can
perform. The right action is the one
that will cause the agent to be most
successful.
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 – at least insofar as its
beliefs permit
42

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
Agents do not have conflicting
goals. Every agent will therefore
always try to do what is asked of it.
43

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
Agents improve performance over
time. They can learn.
44

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
An agent will not knowingly
communicate false information
45

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
An agent is autonomous if its
behaviour is determined by its own
experience (with ability
to learn and adapt)
46

 reactive
 pro-active
 social
 rational
 benevolent
 adaptable
 truthful
 autonomous
 mobile
Mobility: the ability of an agent to
move around an electronic network
47

Design of intelligent agents.
Examples

Settings
49
Performance
measure
Environment
Sensors
Actuators

Settings
50
Performance
measure
Environment
Sensors
Actuators
Designing an automated taxi driver:
Safe, fast, legal, comfortable trip,
maximize profits
Roads, other traffic, pedestrians,
customers
Cameras, speedometer, GPS,
engine sensors, keyboard, etc.
Steering wheel, accelerator, brake,
signals

Settings
51
Performance
measure
Environment
Sensors
Actuators
Designing a medical diagnosis system:
Healthy patient, minimize costs,
lawsuits
Patient, hospital, staff
Keyboard (entry of symptoms,
findings, patient's answers)
Screen display (questions, tests,
diagnoses, treatments, referrals)

Settings
52
Performance
measure
Environment
Sensors
Actuators
Designing an interactive English tutor:
Maximize student's score on test
Set of students
Keyboard
Screen display (exercises,
suggestions, corrections)

Active learning exercise:
“How to implement
reactive agents?
Discuss it with your classmates!”
Image © renjith krishnan at http://www.freedigitalphotos.net/

Implementing deliberative agents.
First steps.

Agent Control Loop Version 1
while true
observe the world;
update internal world model;
deliberate about what intention to
achieve next;
use means-ends reasoning to get a plan
for the intention;
execute the plan
end while
First pass

More formally…

With deliberation…
[…which can be further extended to consider practical reasoning agents…]

Dilbert
Scott Adams
The real state agent (cont.)
58

Homework:
“Search for real-world applications
that use intelligent agents and
describe their tasks and
functioning!”

Assessment

Question
61
(Taken from a public sample exam © Wooldridge)
Briefly define and explain, with examples where
appropriate, THREE properties you would
expect an intelligent agent to exhibit!
PLEASE ANSWER AT:
https://docs.google.com/forms/d/1K0RZur9bDtZs0R0k74GKn7Va34ALPYtXxXUFJH51_1k/viewform

Last active learning exercise

The content

References

Others…
65

Further reading and sites…
 IFAAMAS – International Foundation for
Autonomous Agents and Multiagent Systems
http://ifaamas.org/
 Journal Autonomous Agents and Multi-Agent
Systems
http://link.springer.com/journal/10458
 13th International Conference on Autonomous
Agents ans Multiagent Systems (AAMAS’14), Paris,
France
http://aamas2014.lip6.fr/
66

Slides of the talk per request:
dagmar@monettdiaz.com
Prof. Dr. Dagmar Monett Díaz
monettdiaz
@dmonett
http://monettdiaz.com

Introduction to Agents and Multi-agent Systems (lecture slides)

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