1. Introduction to Semantic Web Rules & Policies
Daniel Olmedilla
L3s Research Center / Hannover University
Programa de Postgrado en Ingeniería Informática y de Telecomunicación (Máster y Doctorado)
Universidad Autónoma de Madrid, 3rd April, 2008

2. About this lecture Why this lecture?
Lot of noise about the Semantic Web
Lot of relevant papers and work on Semantic Web in last years
Techniques and tools can be used in the context of adaptivity, lifelong learning and competence development
Intelligent systems/agents need to be guided
Software agents
Development is expensive
Are static
Are unflexible
Universidad Autónoma de Madrid
Apr. 3rd, 2008

3. About this lecture Objectives
This lecture is intended to provide
reasons that motivated Semantic Web Research
a basic understanding of rule-based representation
a basic introduction to reasoning techniques
a basic understanding of requirements of current distributed systems
a motivation for the use of policies
a basic introduction to rule-based policies and their applications
a basic introduction to reactive policies
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Apr. 3rd, 2008

4. About this lecture Disclaimer
The objective is to present the main ideas
not a full explanation of the theory that lays behind
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Apr. 3rd, 2008

5. About this lecture Interactive
And also important
This is not
a conference presentation
a monologue
Each module partially builds on concepts from previous modules
Exercises are provided in order to strength understanding
You are also encouraged to interrupt and ASK Questions
whenever you need it
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Apr. 3rd, 2008

6. About this lecture The slides
Slides are wordy so they can be easily understood offline after the tutorial
More definitions and references are available in notes and hidden slides
Lecture is available from:
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Apr. 3rd, 2008

7. Outline Lecture Overview
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8. Universidad Autónoma de Madrid
Outline Introduction
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Apr. 3rd, 2008

9. Introduction Warming Up: Problem
Institutions, companies and people need to control the way they
Make business
Take decisions
Offer their assets
Etc …
Computers help us on our daily work performing tasks
that we cannot perform (or we do it worse)
automatically on our behalf
But generally, we need to control how decisions and actions are taken
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Apr. 3rd, 2008

10. Introduction What is a policy?
In a very broad way, a policy is defined as
a statement defining the behaviour of an entity
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Apr. 3rd, 2008

11. Introduction Policies are everywhere
B2B contracts
e.g. quantity flexible contracts, late delivery penalties, etc.
Negotiation
e.g. rules associated with auction mechanisms
Security
e.g. access control policies
Privacy
Information Collection Policies (aka “ P3P Privacy Policies”)
Obfuscation Policies
Workflow management
What to do under different sets of conditions
Context aware computing
What service to invoke to access a particular contextual attribute
Context-sensitive preferences
[ by Norman Sadeh, Semantic Web Policy Workshop panel, ISWC 2005 ]
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Apr. 3rd, 2008

12. Exercise 1 Specify your own policies
How do you decide (in general terms)
which transportation you use to come to the university?
whether you share your
Homework?
Pictures from your holidays in Hawaii?
Your famous report so many companies are willing to pay for?
whether you take a private call when being at work?
which tasks you perform everyday at work?
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13. Universidad Autónoma de Madrid
Exercise 1 Problem (I)
Now imagine a system application or software agent could/should decide on your behalf. How do you tell such an agent how it should do it?
The way we make business, take decisions, etc.
Is dynamic, that is, often changes
Evolves with the time
We cannot re-code, re-compile, re-install a new software agent every time we change the way we take decisions
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14. Universidad Autónoma de Madrid
Exercise 1 Problem (II)
Furthermore, we need that the system acting on our behalf
does what we want
How do we tell it?
What if we make a mistake and tell something wrong?
is contextual, that is, depends on many factors
is “intelligent” (does things as we would do them)
is not reserved only to millionaires 
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15. Universidad Autónoma de Madrid
Introduction The goal
Build applications/agents where
Behaviour is flexible
Can be changed/updated
without re-coding, re-compiling, re-installing, etc…
In a costless manner
Can be managed by administrators/users without needing to be computer experts
Can be understood by normal users
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16. Outline Why the Semantic Web?
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17. Why the Semantic Web? HTML: in your browser
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18. Why the Semantic Web? HTML: Markup
<h2> Topics </h2> <p> Educational Principles <br/> Knowledge Management <br/> Education Process Modeling <br/> Learning Design <br/> Competence Development <br/> … </p> <h2> Lecturers </h2> <p> Albert Angehrn, INSEAD, France <br/> Boyan Bontchev, Sofia University, Bulgaria <br/> Alexandar Dimov, Sofia University, Bulgaria <br/> Dai Griffiths, University of Bolton, United Kingdom <br/> … </p>
Markup for
presentation only
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Apr. 3rd, 2008

19. Why the Semantic Web? HTML: Limitations
HTML deals only with formatting of data
It does not provide information about the data it contains
Query engines do a great job but queries like
Give me the list of subjects that the winter school will deal with
Return the affiliations of the lecturers in the winter school
are not possible on the current Web
Search on current Web is based on syntactic matching
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20. Why the Semantic Web? Current Web
Downloadable Resources:
identified by URL's
untyped
Links:
href, src, ...
limited, non-descriptive
User:
Exciting world
semantics of the resource, however, gleaned from content
Machine processable:
Very little information available
significance of the links only evident from the context around the anchor.
[Eric Miller. Weaving Meaning : An Overview of The Semantic Web ]
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21. Why the Semantic Web? Semantic Web Definition
“The 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.”
Tim Berners-Lee, James Hendler, Ora Lassila The Semantic Web, Scientific American, May 17, 2001
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22. Why the Semantic Web? The Semantic Web
Resources (any resource):
Globally Identified by URI's
Extensible
Relational
Links:
Identified by URI's
User:
Even more exciting world, richer user experience
Machine:
More processable information is available (Data Web)
Computers and people:
Work, learn and exchange knowledge effectively
[Eric Miller. Weaving Meaning : An Overview of The Semantic Web ]
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23. Outline Last Year Lecture
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24. Last Year Lecture The Semantic Web Stack
XML / Namespaces
URI / Unicode
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25. Last year lecture RDF foundations
Share basic syntax with other Web standards
URI: unique identifiers
Namespaces: organize/group identifiers
XML: reuse syntax and data types
XML / Namespaces
URI / Unicode
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26. Last year lecture RDF Model
Data model facility
Evolution of hyperlinks
Open, extensible (open world assumption)
Graph model
Easy interconnection of distributed data
XML / Namespaces
URI / Unicode
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27. Last year lecture RDF Schema
Facility for shared vocabulary
Properties to share link types
Classes to share resource types
XML / Namespaces
URI / Unicode
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28. Last year lecture SPARQL
Querying facility
Flexible pattern matching
No reasoning
Some reasoning support
via entailment regime
XML / Namespaces
URI / Unicode
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29. Last year lecture OWL / Description Logic
Reasoning facility
Support complex ontology models
Reasoning on class and instance level
XML / Namespaces
URI / Unicode
ontology complexity
amount of data
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30. Last year lecture The Semantic Web Stack
Part of this
year lecture
XML / Namespaces
URI / Unicode
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31. Last year lecture Warning (or clarification )
OWL: Web Ontology Language
Ontology = OWL
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32. Outline Rule-Based Representation & Reasoning
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33. Rule-Based Representation and Reasoning Who uses logic?
Aristoteles
Spock
Mathematicians
Computer scientists
You
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34. Exercise 1 Revisited (I)
Were your policies
declarative?
That is, they specify the what (conditions) but not the how (algorithm or process to satisfy them)
E.g., HTML pages describe what the page should contain but not how to actually display the page on a computer screen
E.g., a SQL select statement specifies the properties of the data to be extracted from a DB, not the process of extracting the data
using inference rules?
E.g., If destination is in Europe then max price is …
E.g., If distance is less than … then go by train
if not, do you think they are more naturally modelled as rules?
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35. Rule-Based Representation and Reasoning Rules are everywhere (I)
Rules of ethics for robots
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey orders given to it by human beings, except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
[Isaac Asimov. Runaround ]
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36. Rule-Based Representation and Reasoning Rules are everywhere (II)
Declarative
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37. Rule-Based Representation and Reasoning Rules are everywhere (III)
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38. Rule-Based Representation and Reasoning Inference Rule (I)
Relation holding between premises (antecedent) and conclusions (consequent)
The conclusion is said to be inferable (or derivable or deducible) from the premises
We can infer new knowledge
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39. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Deductive vs. Inductive Reasoning
Deductive: proceeds from general principles or premises to derive particular information (conclusions).
Example
All apples are fruit.
All fruits grow on trees.
Therefore all apples grow on trees.
Remember Sherlock Holmes?
Inductive: the premises of an argument are believed to support the conclusion but do not ensure its truth.
Makes generalizations (from empirical observations)
All observed crows are black.
Therefore all crows are black.
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40. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Syntax of First Order Predicate Logic (FOL)
Logical Symbols
punctuation, connectives, quantifiers, variables
Signature Symbols
n-ary function symbols (0-ary = constant)
n-ary relation symbols
Term
x Mary founder (x) founder (Web5.0)
Atom
married(Mary ,Tom) married(founder (y),Tom)
Formula
person(Mary )  person(Tom)  company(Web5.0) 
x (company(x)  person(founder (x) )
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41. Rule-Based Representation and Reasoning Standard Notions (FOL)
Bound/free􀀀
( x [x p(x)  q(x) ]  [ r (x)   x s(x)] )
Closed
no free variables
Ground
no variables
Propositional􀀀
[p  q]  [ r  s]
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42. Rule-Based Representation and Reasoning Inference Rule (II)
Rule notation: consequent ← antecedent
Stands for antecedent  consequent
that is, IF antecedent THEN consequent
Examples:
If someone is a man then he is mortal
mortal(X) ← man(X).
If someone is in this lecture, then he/she is a researcher
researcher(X) ← inThisLecture(X).
It does not matter what X is, the rule is always valid.
Base for deductive reasoning
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43. Rule-Based Representation and Reasoning Logic Programming
Literal
atom A, negated atom A
Clause
A1  …  Ak ← L1  …  Ln atoms Ai , literals Lj , k ≥ 0, n ≥ 0
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TENCompetence WS
Feb. 21st, 2008
Apr. 3rd, 2008 43 43

44. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Example: information about your family
Assume an agent needs to know all the information about your closest relatives.
How do you inform your agent about such information?
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45. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Possibility 1: Enumerate all the facts
Try to enumerate all that information for your agent:
Tom is the father of Mary
Tom is the parent of Mary
Alice is the sister of Mary
Mary is the sister of Alice
Clara is the sister of Mary
Mary is the sister of Clara
Mary is the mother of Anne
Mary is the parent of Anne
Tom is the grandparent of Anne
Alice is the aunt of Anne
Clara is the aunt of Anne
Clara is the mother of Bob
Alice is the aunt of Bob
Mary is the aunt of Bob
Tom is the grandparent of Bob …
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46. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Possibility 2: facts + rules + deduction
Axioms/Facts
Tom is the father of Mary father(‘Tom’,’Mary’).
Alice is the sister of Mary sister(‘Alice’,’Mary’).
Clara is the sister of Mary sister(‘Clara’,’Mary’).
Mary is the mother of Anne mother(‘Mary’,‘Anne’).
Clara is the mother of Bob mother(‘Clara’,‘Bob’).
A parent is either a father or a mother
parent(P,C) ← father(P,C)  mother(P,C).
The parent of your sister is your parent
parent(P,C) ← parent(P,X)  sister(X,C) .
The parent of a parent is a grandparent
grandparent(P,C) ← parent(P,X)  parent(X,C).
An aunt is the sister of a parent
aunt(A,C) ← sister(A,X)  parent(X,C) .
Inference Rules
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47. Universidad Autónoma de Madrid
Rule-Based Representation and Reasoning Exercise 2: deductive reasoning
Given such a program, write down the inferred new knowledge
Tom is the father of Mary father(‘Tom’,’Mary’).
Alice is the sister of Mary sister(‘Alice’,’Mary’).
Clara is the sister of Mary sister(‘Clara’,’Mary’).
Mary is the mother of Anne mother(‘Mary’,‘Anne’).
Clara is the mother of Bob mother(‘Clara’,‘Bob’).
A parent is either a father or a mother
parent(P,C) ← father(P,C)  mother(P,C).
The parent of your sister is your parent
parent(P,C) ← parent(P,X)  sister(X,C) .
The parent of a parent is a grandparent
grandparent(P,C) ← parent(P,X)  parent(X,C).
An aunt is the sister of a parent
aunt(A,C) ← sister(A,X)  parent(X,C) .
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48. Rule-Based Representation and Reasoning Exercise 2: solution
Given such a program, write down the inferred new knowledge
From first rule:
Tom is the parent of Mary parent(‘Tom’,’Mary’).
Mary is the parent of Anne parent(‘Mary’,’Anne’).
Clara is the parent of Bob parent(‘Clara’,’Bob’).
From second rule (+ the first rule):
Tom is the parent of Alice parent(‘Tom’,’Alice’).
Tom is the parent of Clara parent(‘Tom’,’Clara’).
From the third rule (+ the first and second)
Tom is the grandparent of Anne grandparent(‘Tom’,’Anne’).
Tom is the grandparent of Bob grandparent(‘Tom’,’Bob’).
From the forth rule (+ the first rule)
Alice is the aunt of Anne aunt(‘Alice’,’Anne’).
Clara is the aunt of Anne aunt(‘Clara’,’Anne’).
Mary is the aunt of Bob aunt(‘Mary’,’Bob’).
Alice is the aunt of Bob aunt(‘Alice’,’Bob’).
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49. Rule-Based Representation and Reasoning Advantages
Declarative
Infer implicit knowledge
Compact representation
Well-defined semantics
Available proofs
Truths that it establishes are absolute
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50. Rule-Based Representation and Reasoning Disadvantages
Wrongly specified rules  wrong implicit knowledge
It must have some truths in hand before starting
Sometimes you don’t have them all
Sometimes not all is true or false
You need to specify all right rules
Otherwise, underspecified programs
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51. Outline Semantic Web Policies
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52. Semantic Web Policies What is a policy? Definitions
A statement defining the behaviour of an entity
An enforceable, well-specified constraint on the performance of a machine-executable action by a subject in a given situation
A deliberate plan of action to guide decisions and achieve rational outcome(s).
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Apr. 3rd, 2008

53. Semantic Web Policies A broader notion of policy
The term policy covers:
Security/Privacy policies, Trust management
Business rules
Quality of Service directives
Service-level agreements
Communication and conversation policies
and more...
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54. Semantic Web Policies An e-learning scenario (I)
Exploiting agents to support collaborative learning in an on-line learning community:
They offer means to handle this complex setting as we will learn from the following four scenarios
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55. Semantic Web Policies An e-learning scenario (II)
“Only my tutor is able to access my homework. My fellow students are able to access my lecture notes but not my homework.”
Access control
Security
Trust management
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Apr. 3rd, 2008

56. Semantic Web Policies An e-learning scenario (III)
“I want to be reminded two days before my homework is due.”
“I want to get an SMS if my tutor extends a homework’s deadline.”
 Reactive Agents
Events (e.g., deadline
extension) trigger
agent decisions
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Apr. 3rd, 2008

57. Semantic Web Policies An e-learning scenario (IV)
“While using my e-learning tool I only want to receive chat messages from my fellow students and my tutor. Others get an automatic reply ‘Please contact me later, I am busy’.”
 Communication Control
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58. Semantic Web Policies An e-learning scenario (V)
“In order to purchase learning material I use my Credit Card only with parties providing the ‘Online Security Certificate’.”
Agent Negotiations
Privacy
Step 4
Step 3
Step 2
Step 1
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Apr. 3rd, 2008

59. Semantic Web Policies An e-learning scenario: Using policies
the whole system becomes more flexible
for different behavior change the policy (not the whole software)
communication in the community gets more personalized
“My fellow students should not disturb me when I am at work.”
automatically generated explanations
“You cannot send me a chat message because …”
“Your tutoring agent alerts because …”
“You cannot access your fellow’s homework because …”
policies are reactive
“As soon as I idle for two days, send me …”
“If a deadline is extended then …”
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60. Semantic Web Policies Naturally expressed as rules
If customers are younger than 26 give a 20% discount on international tickets
Up to 15% of network bandwidth can reserved if payment is done with an accepted credit card
Customers can rent a car if they are 18 or older, and exhibit a driving license and a valid credit card
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61. Semantic Web Policies Benefits
Explicit license for autonomous behaviour
Reusability
Efficiency
Extensibility
Context-sensitivity
Verifiability
Support for simple as well as sophisticated agents
Protection from poorly-designed, buggy or malicious agents
Reasoning about agent behaviour
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62. Semantic Web Policies Requirements
Many policies, one framework
Integration with external sources
Policies as active objects
Executing actions
Negotiations
User awareness and control
Cooperative enforcement
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63. Semantic Web Policies Many policies, one framework
It is appealing to integrate all policies in one framework
One common infrastructure
for interoperability and decision making
Where policies can be harmonized & coordinated
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64. Exercise 1 Revisited (II)
Were your policies
requiring extra knowledge
Who are your colleagues and your professors
Who works in your project
What a valid credit card is
Distance between XYZ and the university is …, XYZ is in Madrid, I can not take the car if …, time required for the trip from XYZ to the university would be …
referencing to properties of requesters?
Sources of this information?
All in our knowledge base?
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65. Semantic Web Policies Integration with external systems
Policies are not islands
Decisions need data, information, and knowledge
Each organization has its own
Already available through legacy software and data
A realistic solution must interoperate with them
Third parties
Credit card sites for validity checking
External databases
Variety of web resources
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66. Semantic Web Policies Policies are not only passive objects
Policies may specify
Exchange of signed information (e.g., digital credentials)
Event logging
Failed transactions must be logged
Log downloads of new articles for one week
Communications and notifications
Notify the administrator about repeated login failures
Workflow triggering
such as (partly) manual registration procedures
i.e. Policies may specify actions
To be interleaved with the decision process
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67. Semantic Web Policies Negotiations
Alice
Bob
Step 1: Alice requests a service from Bob
Step 2: Bob discloses his policy for the service
Step 3: Alice discloses her policy for VISA
Step 4: Bob discloses his BBB credential
Step 5: Alice discloses her VISA card credential
Step 6: Bob grants access to the service
Service
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68. Exercise 1 Revisited (III)
Suppose
Your policy is given to you by your employer
You have to explain your policy
You submit a paper and you get “Rejected”
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69. Semantic Web Policies User awareness and control
Explain policies and system decisions
Make rules & reasoning intelligible to the common user
Encourage people to personalize their policies
Make it easy for users to write their own rules
Use natural language?
“Academic users can download the files in folder historical_data whenever their creation date precedes ”
Suitably restricted to avoid ambiguities
Fortunately, users spontaneously formulate rules
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70. Semantic Web Policies Cooperative Policy Enforcement
Crucial for the success of a service
Never say (only) “no”!
Encourage first-time users
Who don't know how to use your service
Explain policy decisions
Especially failures
Advanced queries: Why not
Advanced queries: How-to, What-if
You can’t open this door, but you can ask Alice for permission
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71. Semantic Web Policies Some solutions already available
Features available out of the box:
Expressive policy languages and frameworks
Integrated relational databases, RDF stores, file systems requests, time and location-aware packages, etc.
Execution of actions such as logging facilities, exchange of credentials, etc.
Policy driven negotiations and preferences
Automatically generated explanations
Demo at
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72. Outline Reactive Policies
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73. Reactive Policies Event-Condition-Action (ECA) Policies
provide a more flexible notion of policies
so far, policies were not able to react,
i.e., to handle events
so far, actions where only included as internal or provisional actions not as a re-action
usually of the form
ON event
IF condition
DO action
ON receiving new call IF user not available DO automatic reply
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74. Reactive Policies Events
trigger the execution of a rule
can be simple events:
e.g., “ON receiving new call”
or more complex
ON receiving new call and at the same time another call comes in and there were no calls in the last 10 minutes
 to define complex events we need an event algebra
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75. Reactive Policies Event algebra
Assume events happen at a certain point of time
The algebra allows us to combine events to create more complex ones
Example operators:
Both E1 and E2 happens at the same time
E1 happened before E2
m events out of n happened in an arbitrary order
E1 and E2 occurred and E2 did not occur …
the complexity of the event algebra used depends on the purpose of the ECA-based system
events have to be stored in a history in order to check against complex combination of events
algorithms for the detection and tracing of complex events are non trivial
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76. Reactive Policies Conditions
handled like the policies we had so far:
they may include external actions to prove the condition
such as a database or web service query
e.g., “… if the there is snow in Innsbruck … ”
they may include negotiations to prove the condition
such as “…if Credit Card is valid …”
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77. Reactive Policies Actions
could be single actions
could also be combinations of actions
Sequential execution
Do Action1 and then Action2 and then Action3
Parallel execution
Do Action1 and Action2 at the same time
More complex combinations possible
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Apr. 3rd, 2008

78. Reactive Policies Do you use them?
Do you think this is nothing you need to know about?
Do you think you have never used this?
Do you think this is too complicated for any user to use?
Does this sound familiar to you?
ON new arrival
IF subject contains “[SPAM]”
DO move to folder “filtered_spam”
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Apr. 3rd, 2008

79. Reactive Policies Many other applications
DB triggers: incremental maintenance of data (databases, XML, RDF, etc.)
cleansing of input data streams
automatic repairs in case of constraint violation
broadcasting of changes in documents to subscribers
maintaining statistics about website usage
Active databases (update correlated fields in case others are updated)
network management
business processes (specification and implementation)
And many more
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Apr. 3rd, 2008

80. Reactive Policies A communication example
Problem: The behavior of a messenger is not well adjustable:
Most of you probably know this: 
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Apr. 3rd, 2008

81. Reactive Policies Problem
arbitrary people bother you with chat messages
they may even call you
for some of them you want to offer an answering machine
some you just want to block
people send you files – how could you trust them?
the messenger allows other calls while you are currently answering a call
although your messenger stores the birthdays of your friends you forget about them because it does not remind you.
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Apr. 3rd, 2008

82. Reactive Policies A possible solution with ECA-policies (I)
Why not use ECA-policies to let your agent solve the problem for you?
ON new receiving call
IF caller is a friend of mine
AND
there is no other currently ongoing call
DO accept call AND put it on the speakers
ON new receiving file
IF sender is a friend of mine OR
sender provides a certificate AND certificate is valid
DO accept file AND store it on folder “received_files”
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Apr. 3rd, 2008

83. Reactive Policies A possible solution with ECA-policies (II)
Even an automatic birthday reminder 
ON new day (timer raised once per day)
IF there is a person in the winter school list AND it is his/her birthday today
DO send a chat message with text “Happy Birthday”
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Apr. 3rd, 2008

84. Reactive Policies Exercise 3: ECA Policies
See given exercise sheet
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Apr. 3rd, 2008

85. Reactive Policies Exercise 3: Solution
Actions executed
Pop up window with a reminder about the exam registration
First call to my skype client
Second call to my wife’s phone
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Apr. 3rd, 2008

86. Outline Conclusions/Summary
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Apr. 3rd, 2008

87. Conclusions Summary (I)
Hopefully this tutorial helped you to get a brief idea about
reasons that motivated Semantic Web Research
a basic understanding of rule-based representation
a basic introduction to reasoning techniques
a basic understanding of requirements of current distributed systems
a motivation for the use of policies
a basic introduction to rule-based policies and their applications
a basic introduction to reactive policies
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Apr. 3rd, 2008

88. Conclusions Summary (II)
Everyday systems/agents take over new tasks we would otherwise perform ourselves
They can do some/many of them faster and better than us
But they are not “intelligent” as we are
We need to tell them what to do/how to behave
Rule-based Policies + reasoning help you to do that
Dynamically and allowing evolution
Flexibly
With well defined semantics and interoperability
At low cost
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Apr. 3rd, 2008

89. Conclusions Summary (III)
But
That brings in many new issues like
Required expressiveness for an application scenario
Usability problems
User Awareness
Verification/validation of policies …
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Apr. 3rd, 2008

90. Universidad Autónoma de Madrid
References
RDF Primer
Antoniou et al., Rule-based policy specification. Secure Data Management in Decentralized Systems. Springer,
Bonatti, Olmedilla. Rule-based policy representation and reasoning for the semantic web. In Reasoning Web, Third International Summer School Springer.
Antoniou et al. (Eds.): Reasoning Web Springer LNCS 4636, pp.1–153
Bradshaw et al., Making Agents Acceptable to people, Intelligent technologies for information analysis: Advances in agents, data mining and statistical learning. Springer
De Coi et al., Exploiting policies in an open infrastructure for lifelong learning. In EC-TEL, Crete, Greece, Sep Springer.
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Apr. 3rd, 2008

91. Questions? Thanks! olmedilla@L3S.de – http://www.olmedilla.info/
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Apr. 3rd, 2008