DAI in Mobile Environment: Course Introduction

Report
TLI371 – Distributed Artificial Intelligence
in Mobile Environment
Course Introduction
Vagan Terziyan
Department of Mathematical Information Technology
University of Jyvaskyla
[email protected] ; [email protected]
http://www.cs.jyu.fi/ai/vagan
+358 14 260-4618
Contents





Course Introduction
Lectures and Links
Course Assignment
Course Exercise
Examples of course-related
research
2
Practical Information
 8 Lectures (2 x 45 minutes each, in English) during period
21 November - 13 December according to the schedule;
 Slides: available online plus hardcopies will be given;
 Exercise (Write 3-5 pages description of possible solution
of a given research problem);
 Assignment (Make PowerPoint presentation file based on
a research paper. Oral presentation is not required);
 Exercise and assignment should be sent electronically to
the lecturer until 13 December (24:00);
 Exam 16 December, 12:00. There will be no exam for students
who will submit the course exercise and assignment within the
deadline and at least of satisfactory quality. Course mark in this
3
case will be given based on the exercise and assignment.
Lectures Topics and Schedule
21 November 2002 – DAI Introduction and Web Content Personalization (today)
Lecture 0 - ”DAI in Mobile Environment: Course Introduction”
Lecture 1 - ”Web Content Personalization Overview”
22 November 2002 – Overview of Intelligent Agents
Lecture 2 - ”What is an Intelligent Agent ?”
28 November 2002 – Overview of (Multi)Agent Technologies I
Lecture 3 - ”Agent Technologies (1)”
29 November 2002 – Overview of (Multi)Agent Technologies II
Lecture 4 - ”Agent Technologies (2)”
5 December 2002 – Overview of Filtering Techniques for Personalization
Lecture 5 - ”Collaborative Filtering”
6 December 2002 – Probabilistic Networks for Personalization *
Lecture 6 - ”Introduction to Bayesian Networks”
12 December 2002 – Profile and Location-Based Personalization with Agents
Lecture 7 - ”Dynamic Integration of Virtual Predictors”
13 December 2002 – Metamodels for Profile Management in Mobile Commerce
Lecture 8 - ”Metamodels for Managing Knowledge”
* - selfstudy
4
DAI in Mobile Environment (example)
Mobile
Customer
Mobile
Customer
Agent
(Peer)
Mobile
Customer
Agent
(Peer)
Agent
(Peer)
Agent
(Peer)
Mobile
Customer
5
Distributed Artificial Intelligence
[A. Tveit, DAI Course]


DAI is a sub-field of AI
DAI is concerned with problem solving where agents
solve (sub-) tasks
Distributed AI

Main areas of DAI
1.
2.
Distributed
Computing
Artificial
Intelligence
Multi-Agent Systems
Distributed Problem Solving
Distributed
Problem
Solving
Multi-Agent
Systems
6
Distributed AI Applications
DISTRIBUTED
AI
Perspectives
Application Area
Web Content Management
Agent
Theory
Gr
ou
p
ge
ng
ua
La
Personalization
igne
rative
Delibe
Emerging Application
re
ctu
e
hes
c
pproa
Des
it
ch
Ar
React
ive
ic A
Specif
r
Agent
technologies
bri
Hy
d
Coop
erati
on
Solutions
ation
ordin
Co
Beliefs
management
at
lic
p
Ap
i
s
on
aly
An
De
sig
n
Distributed
transactions
management
ols
Ne
Co
h
Beh erent
avi
or
Methods
sis
To
Filtering
ion
t
tia
go
Planning
Profile / Location
management
eds
Testb
Knowledge
metamodeling
Data
mining
7
Introduction:
Semantic Web - new Possibilities for
Intelligent Web Applications
8
Motivation for Semantic Web
Semantic Web Structure
Before Semantic Web
Semantic
Annotations
Ontologies
Logical Support
Languages
Tools
Applications /
Services
Semantic
Web
WWW
and
Beyond
Creators
Users
WWW
and
Beyond
Web content
7
Creators
Users
Web content
9
8
Semantic Web Content: New “Users”
Semantic
Web and
Beyond
Users
Creators
applications
Semantic Web
content
agents
Semantic
Annotations
Ontologies
Logical Support
Languages
Tools
Applications /
Services
Semantic
Web
WWW
and
Beyond
Creators
Users
Web content
10
Some Professions around Semantic Web
AI Professionals
Content creators
Content
Mobile Computing
Professionals
Ontologies
Agents
Logic, Proof
and Trust
Web designers
Annotations
Ontology engineers
Software engineers
11
Semantic Web: Resource Integration
Semantic
annotation
Shared
ontology
Web resources /
services / DBs / etc.
12
What else Can be Annotated
for Semantic Web ?
External world
resources
Web resources /
services / DBs / etc.
Web users
(profiles,
preferences)
Shared
ontology
Web agents /
applications
Web access
devices
13
Word-Wide Correlated Activities
Semantic Web
Semantic Web is an extension of the current
web in which information is given well-defined
meaning, better enabling computers and people
to work in cooperation
Agentcities is a global, collaborative effort
to construct an open network of on-line systems
hosting diverse agent based services.
Agentcities
Grid Computing
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.
Web 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.
14
University of Jyvaskyla Experience:
Examples of Related Courses
Digitaalisen median erityiskysymyksiä (2 ov)
seminaarin aihepiiri:
Semanttinen web
Structured Electronic Documentation
Lecturer: Matthieu Weber
Lecturer: Airi Salminen
University of Jyvaskyla, CS & IS Department, Spring 2002
18
University of Jyvaskyla, MIT Department, Fall 2001, 2002
[email protected]
18
DAI Course: Lectures
16
Lecture 0: This Lecture - DAI Introduction
http://www.cs.jyu.fi/ai/vagan/DAI_Introduction.ppt
17
Lecture 1: Web Content Personalization Overview
http://www.cs.jyu.fi/ai/vagan/Personalization.ppt
18
Lecture 2: What is an Intelligent Agent ?
Ability to Exist to be Autonomous,
Reactive, Goal-Oriented, etc.
What is an Intelligent Agent ?
- are the basic abilities of an Intelligent Agent
Based on Tutorials:
Monique Calisti, Roope Raisamo
http://www.cs.jyu.fi/ai/vagan/Agents.ppt
19
Lectures 3-4: Agent Technologies
Mobility and Flexibility, Abilities to Communicate,
Cooperate, and Negotiate with other Agents - are
among the basic abilities of an Intelligent Agent
Agent Technologies
Based on tutorials: Monique Calisti, Amund Tveit, Shaw Green, Leon Hurst,
Brenda Nangle, Pádraig Cunningham, Fergal Somers, Richard Evans
2
1
http://www.cs.jyu.fi/ai/vagan/Agent_Technologies.ppt
20
Lecture 5: Collaborative Filtering
http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt
21
Lecture 6: Introduction to Bayesian Networks
http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt
22
Lecture 7: Dynamic Integration of Virtual Predictors
http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt
23
Lecture 8: Metamodels for Managing Knowledge
http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt
24
DAI Course: Exercise
25
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 as26fire,
earthquake, flood protection, etc.
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?
27
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?
28
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. Including
figures, it should be at least 3 to 5 pages long.
29
Format, Submission and Deadlines
 Format: MS Word doc. (winzip encoding
allowed), name of file is student’s family name;
 Presentation should contain all references to the
materials used;
 Deadline - 13 December 2002 (24:00);
 Files with presentations should be sent by e-mail
to Vagan Terziyan ([email protected]);
 Notification of evaluation - until 14 December
(16:00).
30
DAI Course: Assignment
31
Assignment in brief
 Students are expected to select one of below
recommended papers, which is not already
selected by some other student, register his/her
choice from the Course Lecturer and make
PowerPoint presentation based on that paper.
The presentation should provide evidence that a
student has got the main ideas of the paper, is
able to provide his personal additional
conclusions and critics to the approaches used.
32
Evaluation criteria for the assignment
 Content and Completeness;
 Clearness and Simplicity;
 Discovered Connections to DAI Course
Material;
 Originality, Personal Conclusions and Critics;
 Design Quality.
33
Format, Submission and Deadlines
 Format: PowerPoint ppt. (winzip encoding
allowed), name of file is student’s family name;
 Presentation should contain all references to the
materials used, including the original paper;
 Deadline - 13 December 2002 (24:00);
 Files with presentations should be sent by e-mail
to Vagan Terziyan ([email protected]);
 Notification of evaluation - until 14 December
(16:00).
34
Papers for Course Assignment (1)
 Paper 1: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_1_P.pdf
 Paper 2: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_2_P.pdf
 Paper 3: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_3_CF.pdf
 Paper 4: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_4_CF.pdf
 Paper 5: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_5_MW.pdf
 Paper 6: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_6_BN.pdf
 Paper 7: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_7_BN.pdf
 Paper 8: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_8_MM.pdf
35
Papers for Course Assignment (2)
 Paper 9: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_9_WM.pdf
 Paper 10: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_10_WM.pdf
 Paper 11: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_11_III.pdf
 Paper 12: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_12_III.pdf
 Paper 13: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_13_KM.pdf
 Paper 14: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_14_ES.pdf
 Paper 15: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_15_MDB.pdf
 Paper 16: http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_16_MDB.pdf
36
University of Jyvaskyla Experience:
Examples of Course-Related Research
37
Mobile Location-Based Service
in Semantic Web
M-Commerce LBS system
Adaptive interface for MLS client
http://www.cs.jyu.fi/~mmm
In the framework of the Multi Meet Mobile
(MMM) project at the University of Jyväskylä,
a LBS pilot system, MMM Location-based
Service system (MLS), has been developed.
MLS is a general LBS system for mobile
users, offering map and navigation across
multiple geographically distributed services
accompanied with access to location-based
information through the map on terminal’s
screen. MLS is based on Java, XML and uses
dynamic selection of services for customers
based on their profile and location.
Virrantaus K., Veijalainen J., Markkula J.,
Katasonov A., Garmash A., Tirri H., Terziyan V.,
Developing GIS-Supported Location-Based
Services, In: Proceedings of WGIS 2001 - First
International Workshop on Web Geographical
Information Systems, 3-6 December, 2001, Kyoto,
Japan, pp. 423-432.
19
Only predicted services, for the customer with known profile
and location, will be delivered from MLS and displayed at
the mobile terminal screen as clickable “points of interest” 20
Route-based personalization
38
Static Perspective
Dynamic Perspective
21
Mobile Transactions Management
in Semantic Web
Web Resource/Service Integration:
Web Resource/Service Integration:
Server-Based Transaction Monitor
Mobile Client-Base Transaction Monitor
Web
resource /
service
Web
resource /
service
Server
Client
TM
wireless
Client
Server
wireless
wireless
TM
Web
resource /
service
Web
resource /
service
Transaction Service
Server
The conceptual
scheme of the
ontology-based
transaction
management
with multiple eservices
Web Resource/Service Integration:
Comparison of Architectures
 Server-based TM




Less wireless (sub)transactions
Rich ontological support
Smaller crash, disconnection
vulnerability




 Client-based TM
Positive:




Negative:
Pure customer’s trust
Lack of customer’s awareness and
control
Problematic TM’s adaptation to the
customer
Positive:
Customer’s firm trust
Customer’s awareness and
involvement
Better TM’s adaptation to the
customer




Server
20
Client 1
Transaction data
Client r
Services data
Transaction data
Negative:
More wireless (sub)transactions
Restricted ontological support
High crash, disconnection
vulnerability
22
Services data
Parameter 1 Recent value
Service 1 ********
Parameter 1 Recent value
Service 1 ********
Parameter 2 Recent value
Service 2 ********
Parameter 2 Recent value
Service 2 ********
…
…
…
Parameter n Recent value
…
Service s ********
Transaction monitor
…
…
Parameter n Recent value
…
Service s ********
Transaction monitor
Ontologies
Service atomic action ontologies
Parameter ontologies
Parameter 1
Name 1
Default value / schema 1
Parameter 2
Name 2
Default value / schema 2
…
…
…
Parameter n
Name n
Default value / schema n
input parameters
input parameters
Name of action 1
Name of action 2
Service 1
output parameters
output parameters
Service s
Subtransaction monitor
Service Tree
input parameters
Name of action k
…
output parameters
Terziyan V., Ontology-Driven
Transaction Monitor for Mobile
Services, In: Proceedings of
[email protected] Workshop on
Formal Ontology, Knowledge
Representation and Intelligent
Systems for the World Wide Web,
Toulouse, France, 19-20 April,
2002.
21
Subtransaction monitor
Clients data
Service Tree
Clients data
Client 1 ********
Client 1 ********
Client 2 ********
Client 2 ********
…
Client r ********
…
…
Client r ********
23
39
P-Commerce in Semantic Web
Clients
Public merchants,
public customers, public
information providers
…
External
Environment
Server
Maps
Maps
<path network>
<business points>
SMOs
I
I
C
S
I
MetaProfiles
XML
WML
SMRs
…
Map Content
Providers
Server
Integration,
Analysis,
Learning
Location
Providers
Server
I
Business
knowledge
Profiles
…
Negotiation,
Contracting,
Billing
XML
…
Content
Providers
Server
…
$ $ $ Banks
Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling
Framework, IJCAI-2001 International Workshop on "E-Business and the
Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.
40
Semantic Metanetwork for Metadata
Management
A' '
2
L ''
L ''
1
S e cond le v e l
2
A' '
3
A' '
1
A'
L'
A'
A'
1
L'
3
A'
2
1
L'
2
L 1
A
2
4
Firs t le v e l
3
L 2
L 4
Z e ro le v e l
A
1
L 3
A
3
Semantic Metanetwork is
considered formally as the
set of semantic networks,
which are put on each other
in such a way that links of
every previous semantic
network are in the same
time nodes of the next
network.
In a Semantic Metanetwork
every higher level controls
semantic structure of the
lower level.
Terziyan V., Puuronen S., Reasoning with Multilevel
Contexts in Semantic Metanetworks, In: P. Bonzon, M.
Cavalcanti, R. Nossun (Eds.), Formal Aspects in Context,
Kluwer Academic Publishers, 2000, pp. 107-126.
41
Petri Metanetwork for Management Dynamics
P´3
P´2
t´1
P´1
t´3
P´4
Controlling
level
P´5
t´2
t1
Basic level
P1
P2
• Each level of the new
structure is an ordinary petrinet
of some traditional type.
• A basic level petrinet
simulates the process of some
application.
P4
P3
•A metapetrinet is able not only
to change the marking of a
petrinet but also to reconfigure
dynamically its structure
t2
Terziyan V., Savolainen V., Metapetrinets for
Controlling Complex and Dynamic Processes,
International Journal of Information and Management
Sciences, V. 10, No. 1, March 1999, pp.13-32.
• The second level, i.e. the
metapetrinet, is used to simulate
and help controlling the
configuration change at the
basic level.
42
Bayesian Metanetwork for Management Uncertainty
Two-level Bayesian Metanetwork for
managing conditional dependencies
Two-level Bayesian Metanetwork for
managing conditional dependencies
Contextual level
A
X
Q
B
Y
X
A
Predictive level
S
R
Q
B
S
Y
R
2-level Bayesian Metanetwork for
modelling relevant features’ selection
Contextual level
Predictive level
Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content
Personalization, In: Proceedings of 2nd WSEAS International Conference on
Automation and Integration (ICAI’02), Puerto De La Cruz, Tenerife, December 2002.
43
Multidatabase Mining based on Metadata
Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with
an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.),
Database and Expert Systems Applications, Lecture Notes in Computer
Science, Springer-Verlag, V. 1677, 1999, pp. 882-891.
44

similar documents