Introduction to Agent Technology in Mobile Environment Course Introduction

Vagan TerziyanDepartment of Mathematical Information Technology

University of Jyvaskyla

vagan@it.jyu.fi ; terziyan@yahoo.comhttp://www.cs.jyu.fi/ai/vagan

+358 14 260-4618

ITIN, France, February 2006

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Contents

Practical InformationCourse IntroductionLectures and LinksCourse Exercise and self-study

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Practical Information

Lectures: 10 hours Thursday: 23 February, 9:00-10:15; 10:30-12:00; 13h30-15h15;

Friday: 24 February, 9:00-10:15; 10:30-12:00. Slides available online (links from Introductory Lecture)

Exercise: 6 hoursThursday: 23 February, 15:30-17:00

Friday: 24 February, 13:30-15:15; 15:30-17:00. task will be announced during the lectures

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Introduction:Semantic Web - new Possibilities for

Agent-Driven Applications

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MobileCustomer

Agent(Peer)

Agent(Peer)

Agent(Peer)

Agent(Peer)

M obileC ustom er

M obileC ustom er

M obileC ustom er

Agents in Mobile Environment (sample scenario)

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Motivation for Semantic Web

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Web Limitations

Doubles in sizeevery six months

Average WWW searches examineonly about 25% of potentially

relevant sites and return a lot ofunwanted information

Information on web is not suitablefor software agents

World Wide Web

Semantic Web

The Semantic Web is avision: the idea of havingdata on the Web defined andlinked in a way that it can beused by machines not just fordisplay purposes, but forautomation, integration andreuse of data across variousapplications.

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B e f o r e S e m a n t i c W e b

W e b c o n t e n t

U s e r sC r e a t o r sW W Wa n dB e y o n d

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S e m a n tic W e b S tru c tu re

S e m a n ticA n n o ta tio n s

O n to lo g ie s L o g ic a l S u p p o rt

L a n g u a g e s T o o ls A p p lic a tio n s /S e rv ic e s

W e b c o n te n t

U se rsC re a to rsW W Wa n dB e y o n d

S e m a n ticW e b

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Semantic Web Content: New “Users”

SemanticAnnotations

Ontologies Logical Support

Languages Tools Applications /Services

Web content

UsersCreatorsWWWandBeyond

SemanticWeb

Semantic Webcontent

UsersSemanticWeb andBeyond

Creators

applications

agents

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Semantic Web: Resource Integration

Shared ontology

Web resources / services / DBs / etc.

Semantic annotation

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Semantic Web: What to Annotate ?Semantic Web: What to Annotate ?

Web resources / services / DBs / etc.

Shared ontology

Web users (profiles,

preferences)

Web access devices

Web agents / applications

External world resources

Smart machines and devices

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Word-Wide Correlated Activities

Semantic Web

Grid Computing

Web Services

Agentcities

Agentcities is a global, collaborative effort to construct an open network of on-line systems

hosting diverse agent based services.

WWW is more and more used for application to application communication.The programmatic interfaces made available are referred to as Web services.

The goal of the Web Services Activity is to develop a set of technologies in order to bring Web services to their full potential

FIPA

FIPA is a non-profit organisation aimed at producing standards for the interoperation

of heterogeneous software agents.

Semantic Web is an extension of the currentweb in which information is given well-definedmeaning, better enabling computers and people

to work in cooperation

Wide-area distributed computing, or "grid” technologies, provide the foundation to a number of large-scale efforts

utilizing the global Internet to build distributed computing and communications infrastructures.

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GENI – Next Generation Internet GENI - Global Environment for Networking Investigations

(proposed 25 August 2005) The U.S. National Science Foundation (NSF) has proposed a

next-generation Internet with built-in security and functionality that connects all kinds of devices, with researchers challenging the government agency to look at the Internet as a "clean slate."

The GENI Initiative envisions the creation of new networking and distributed system architectures that, for example:

Build in security and robustness; Enable the vision of pervasive computing and bridge the gap between

the physical and virtual worlds by including mobile, wireless and sensor networks;

Enable control and management of other critical infrastructures; Include ease of operation and usability; and Enable new classes of societal-level services and applications.

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GUN vs. GENIGUN vs. GENI

GUN initiative intends to provide tools and solutions to make heterogeneous industrial resources (files, documents, services, devices, processes, systems, human experts, etc.) web-accessible, proactive and cooperative in a sense that they will be able to analyze their state independently from other systems or to order such analysis from remote experts or Web-services to be aware of own condition and to plan behavior towards effective and predictive maintenance.

GUNGUN

GGloballobal

UUnderstandingnderstanding

eeNNvironmentvironmentAgent Technologies is a key advantage !

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Resource Resource HistoryHistory

Ontology

Templates

RolesGoals

Behaviour rules

Resource Resource AgentAgent

Behaviour

Templates

Executable Executable modules or modules or

Web ServicesWeb Services

RGBDF on a GUN PlatformRGBDF on a GUN Platform

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One of Smart Resource ScenariosOne of Smart Resource Scenarios

““Expert”Expert”

““Service”Service”

Labelled data

Labelled data

Diagnostic model

Que

ryin

g di

agno

stic

Que

ryin

g di

agno

stic

resu

ltsre

sults

Labelled data

Labelled data

Wat

chin

g a

nd

qu

eryi

ng

dia

gn

ost

ic d

ataLa

belle

d da

ta

Labe

lled

data

History data

““Device”Device”

Querying data for

learning

Learning sample and

Learning sample and

Querying diagnostic results

Querying diagnostic results

““Knowledge Transfer Knowledge Transfer from Expert to Service”from Expert to Service”““Knowledge Transfer Knowledge Transfer

from Expert to Service”from Expert to Service”

Agent plays roles:

Scene 1: “patient”;Scene 2: “teacher”;

Scene 3: “patient”

Agent plays roles:

Scene 1: “diagnostic expert”;Scene 2: “no play”;Scene 3: “no play”

Agent plays roles:

Scene 1: “no play”;Scene 2: “student”;

Scene 3: “diagnostic expert”

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ATME Course: Lectures

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Semantic Web Lectures

Lectures Schedule

23/02/2006 (9:00 - 10:15) – Lecture 1: What is an Intelligent Agent?

23/02/2006 (10:30 - 12:00) – Lecture 2: Agent Technologies

23/02/2006 (13:30 - 15:15) – Lecture 3: Agent Architectures

24/02/2006 (9:00 - 10:15) – Lecture 4: Mobile Personalization with Agents

24/02/2006 (10:30 - 12:00) – Lecture 5: Industrial Agent-Driven Smart Resources

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Introduction

http://www.cs.jyu.fi/ai/vagan/AT_Introduction.ppt

Introduction to Agent Technology in Mobile Environment

Course Introduction

Vagan TerziyanDepartment of Mathematical Information Technology

University of Jyvaskyla

vagan@it.jyu.fi ; terziyan@yahoo.comhttp://www.cs.jyu.fi/ai/vagan

+358 14 260-4618

ITIN, France, February 2006

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Lecture 1: What is an Intelligent Agent ?

http://www.cs.jyu.fi/ai/vagan/Agents.ppt

Ability to Exist to be Autonomous,Reactive, Goal-Oriented, etc.

- are the basic abilities of an Intelligent Agent

What is an Intelligent Agent ?

Based on Tutorials:Monique Calisti, Roope Raisamo

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Lecture 2: Agent Technologies

http://www.cs.jyu.fi/ai/vagan/Agent_Technologies.ppt

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Mobility and Flexibility, Abilities to Communicate,Cooperate, and Negotiate with other Agents - are

among the basic abilities of an Intelligent Agent

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Agent Technologies

Based on tutorials: Monique Calisti, Amund Tveit, Shaw Green, Leon Hurst,Brenda Nangle, Pádraig Cunningham, Fergal Somers, Richard Evans

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Lecture 3: Agent-Based Content Management Architectures

http://www.cs.jyu.fi/ai/vagan/Architectures.ppt

2121 Web Content Management Architectures

Vagan TerziyanMIT Department, University of Jyvaskyla,

AI Department, Kharkov National University of Radioelectronics

vagan@it.jyu.fi; http://www.cs.jyu.fi/ai/vagan/index.html

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Lecture 4: Mobile Personalization with Agents

http://www.cs.jyu.fi/ai/vagan/Mobile_Personalization.ppt

Personalisation in Mobile Environment

Based on papers and presentations of Catholijn Jonker, Vagan Terziyan, Jan Treur, Oleksandra Vitko and others

MIT Department, University of Jyväskylä

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Lecture 5: Industrial Agent-Driven Smart Resources

http://www.cs.jyu.fi/ai/vagan/SmartResource_Summary.ppt

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Additional Material for Self-Study

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Agent Standards

http://www.cs.jyu.fi/ai/vagan/Agent_Standards.ppt

Agent StandardsFIPA Agent Framework

Based on Presentation of Heimo Laamanen(Sonera Corporation)

Ability to Behave in a Standardized W ay Allowing Interoperability with other

Heterogeneous Agents,

- are the basic abilities of an Intelligent Agent

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Designing Software Agents with JADE

http://jade.cselt.it

http://www.fipa.org

http://www.hibernate.org

http://herzberg.ca.sandia.gov/jess/

http://protege.stanford.edu/

www.swi.psy.uva.nl/usr/aart/beangenerator

http://jadex.sourceforge.net

http://www.cs.jyu.fi/ai/vagan/JADE_Agents.ppthttp://jade.tilab.com/doc/JADEProgramming-Tutorial-for-beginners.pdf

http://sharon.cselt.it/projects/jade/papers/JADETutorialIEEE/JADETutorial_Programming.pdfhttp://sharon.cselt.it/projects/jade/papers/JADETutorialIEEE/JADETutorial_Using.pdf

http://www.eclipse.org/

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JADE (Java Agent DEvelopment Framework)

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Related CourseAgent Technologies in the Semantic Webhttp://www.cs.jyu.fi/ai/vadim/ ;by Vadim Ermolayev;recommended as additional reading.

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Course Exercise

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Alternative 1

for software engineers

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Develop Agent(s) with JADE

Try to develop simple agent scenario based on JADE (or JADE+Eclipse) development environment;

Possible scenario: agent which sends e-mails with fixed congratulation text to the persons from the address book who have a birthday;

Any other scenario of your choice will be OK

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Alternative 2no software development, just a report

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Task for the Exercise (according to A. Raja) (1) Consider the home of the future where there are software agents in a

mobile environment that are helping to manage the running of a house. There will be:

(1) Personal assistant agents that will know of your preferences of temperature, humidity, light, sound, etc., and who you want to interact with;

(2) There will be agents that can measure appropriate environmental conditions with specific devices;

(3) There will be agents that effect appropriate environmental conditions with specific devices;

(4) There will be agents that control expenses for the use of appropriate devices;

(5) There will be agents that manage the telephone communications; (6) There will be agents that manage security issues such as fire,

earthquake, flood protection, etc.

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Task for the Exercise (according to A. Raja) (2)Assume that the agents are heterogenous (i.e. have not be

generated by one designer), for example when you get a new device it will come with an agent; for instance, the heating measurement agent may not come from the same company as the air-conditioning agent.

Think about the possibility of having these agents work together. What are the capabilities of the agents, what type of cooperation needs to occur among them, are there needs for the agents to negotiate, are there situations where local objectives are at odds with global objectives such as minimizing electrical usage? What type of information needs to be exchanged among the agents?

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Task for the Exercise (according to A. Raja) (3)

How would you organize the agents – would you have a hierarchy of agents in terms of their control responsibilities? How would you allow agents to integrate new agents into the system, for instance, when you buy a new device.

What are the specific characteristics required by a language in order that these agents can share information? If there are no dedicated resources for each agent, but rather a pool of resources that can be used by agents, what new issues does this introduce? Do agents need to reason about the intentions of other agents?

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Task for the Exercise (according to A. Raja) (4)

In answering these and related issues that you may consider, please be concrete with specific and numerous examples/scenarios. You should first start out the effort by detailing the collection of agents that you see in the house of the future, what their responsibilities are, and their patterns of interaction with other agents.

Outcome of the exercise is report. Including figures, it should be 3-5 pages long.