ΑΝΑΠΑΡΑΣΤΑΣΗ ΓΝΩΣΗΣ ΚΑΙ
ΣΥΛΛΟΓΙΣΤΙΚΗ
(KNOWLEDGE REPRESENTATION
AND REASONING)
Page 1 of 29
Knowledge Pyramid
MetaKnowledge
Information
Data
Noise
Page 2 of 29
20-Apr-04
Lecture 1
30
Data-Information-Knowledge
Hierarchy
„
„
„
Data – collected symbols and
artifacts
Information – the
interpretation of these
artifacts within some context
Knowledge – the integration
of the information into a
knowledge base, so that it
can be effectively utilized
Knowledge
Integration and Usage
Information
Interpretation in Context
Data
Page 3 of 29
20-Apr-04
Lecture 1
31
Data
„
Articfacts - which exist as a vehicle for
conveying information; the basis of information
(e.g. writing, images, sounds)
„
„
„
¾
Physical – marks on a sheet of paper
Electronic – bit patterns in a computer memory
Others – electro-chemical potentials in the brain
In KBS we need to support
¾
¾
Various media formats and data representations
Data must adhere to some structure, which allows
the required information to be extracted
Page 4 of 29
20-Apr-04
Lecture 1
32
Information
„
The interpretation of data within a context
set by a priori knowledge and the current
environment
„
„
Priori knowledge (e.g. language)
Current environment and context (e.g. why, where, how)
„
Identifying mappings from concepts which
already exist in our knowledge base to the
information captured by the data
¾
In KBS we need to support the
extraction/storage/usage of the a priori users
knowledge
¾
creation of the data-information mappings at the full
Page 5 of 29 range of levels
¾
20-Apr-04
Lecture 1
33
Knowledge
„
The base of personal information, which is
integrated in a fashion, which allows it to be
used in further interpretation and analysis of
data.
„
The mappings from data symbols to concepts are only
useful if we are able to use them in some way further
•
•
¾
direct usage in order to make a decision or carrying out
an action
in the augmentation of out knowledge base
In KBS we need to support the
integration of information
Page 6 of
¾ 29 consideration for further usage
¾
20-Apr-04
Lecture 1
34
Data-Information- Knowledge
Data - raw digital material or the “artifacts
which exist as a vehicle for conveying
information”
Information - interpreted data “within a
context set by a priori knowledge and the
current environment”
Knowledge - assigns a purpose and/or
action to information;
information knowledge is based
on “information integrated in a fashion
which allows it to be used in further
interpretation and analysis of data”
Page 7 of 29
20-Apr-04
Lecture 1
35
Noise-Data-Information-Knowledge
{
Consider the following sequence of 24
numbers
z
{
{
137178766832525156430015
Without any knowledge this sequence
appears to be noise
If it is known that this sequence is
meaningful then the sequence is data
Page 8 of 29
Noise-Data-Information-Knowledge
{
Certain knowledge may exist to
transform data into information. For
example the following algorithm:
z
z
z
{
Applying the algorithm we get
z
Page 9 of 29
Group the numbers by twos
Ignore any two-digit numbers less than 32
Substitute the ASCII characters for the
two-digit numbers
GOLD 438+
Noise-Data-Information-Knowledge
{
Now knowledge can be applied to this
information. For example there may be
a rule
z
{
Page 10 of 29
IF gold is less than 500
and the price is rising (+)
THEN buy gold
Expertise is a special kind of
knowledge that experts have
Noise-Data-Information-Knowledge
{
{
{
{
Page 11 of 29
Meta-knowledge is knowledge about
knowledge and expertise
An expert system may be designed
with knowledge about several domains
Meta-knowledge will specify which
knowledge base is applicable
Meta-knowledge can be used within
one domain to decide which group of
rules is applicable
Considerations for
Knowledge-Based Systems
„
„
„
„
Extent to which we are aiming to facilitate
changes in the user’s knowledge base.
How information presented will be
integrated into user’s knowledge
structures
What mechanisms we may provide to
evalaute and support this process
Systems, which help the user use the
information not just retrieve it
Page 12 of 29
20-Apr-04
Lecture 1
36
Knowledge, Information &
Data
The essence of knowledge is:
having it, to apply it;
not having it, to confess your
ignorance
The information is the essence of
knowledge
¾ Having information
¾13Applying
information
Page
of 29
¾
20-Apr-04
Lecture 1
37
Knowledge Based Systems
The goal is to facilitate intelligent
interaction, in a user-oriented fashion,
which is based on:
„
„
„
the identification of the appropriate
information
the effective utilization of the appropriate
information
the user control of the appropriate
information
in order to fulfill specific user goals
Page 14 of 29
20-Apr-04
Lecture 1
38
Knowledge in KBS
„
Knowledge – most important & labor
heavy task within KBS construction:
the experience of an expert
„ the transcription of this experience in KBS
„
• methods - knowledge that describes how to
perform an intellectual process
• domain knowledge - that represents what is
manipulated by the methods
Page 15 of 29
20-Apr-04
Lecture 1
39
Representation & Reasoning
„
knowledge representation language:
„
syntax
• describes the possible configurations that
can constitute sentences
„
semantics
• determines the facts in the world to which
the sentences refer
• tells us what the agent believes
Page 16 of 29
20-Apr-04
Lecture 1
40
Knowledge level
„
„
Level of description of the knowledge of
an system that is independent of the
symbol-level representation used
internally by the system
Knowledge is attributed to systems by
observing their actions
„
„
a system ‘knows’ something if it acts
accordingly and rationally to achieve its goals
‘actions’ of KBS can be seen through a
tell&ask functional interface [Levesque,
1984], logical assertions and queries
Page 17 of 29
20-Apr-04
Lecture 1
41
Symbol
„ Written,
printed mark to
represent something
Object
„ Quality
„ Process
„ Quantity
„ etc.
„
Page 18 of 29
20-Apr-04
Lecture 1
42
Symbols in KBS
„
„
„
„
„
Core of the intelligent behavior in KBS
Physical (written or printed) mark or
pattern representing something in a
selected medium
Organized in symbol structures
Physical symbol systems [Newell &
Simon, 1975]: machine operating on
symbol structures => symbol processing
Symbol level = program level
Page 19 of 29
20-Apr-04
Lecture 1
43
Advantages of symbol systems
„
much of human knowledge is symbolic,
„
„
architecture reasoning is analogous to
how humans reason
„
⇒
encoding it in a computer is more straightforward
making it easier for humans to understand
physical symbol system hypothesis
Page 20 of 29
20-Apr-04
Lecture 1
51
Semantics
Systematic relationship between
symbols (signs), interpreter or model
(signifier) and referents (signified)
„ Types of Semantics:
„
Referential – symbols refer to objects in
the domain by interpretation
„ Denotational – maps symbols onto a
description of computation
„
Page 21 of 29
20-Apr-04
Lecture 1
52
Knowledge Representation
(KR)
„
Symbol system - encodes a body of
knowledge
representation
knowledge
symbols
concepts
relationships
access
representation formalism
computational process
storing and retrieving knowledge
Page 22 of 29
20-Apr-04
Lecture 1
53
Why KR?
„
„
„
„
Basic principles & concepts for KR
Knowledge ÅÆ Information ÅÆ Data
Meaning
KR methods
„
„
logic, rules, semantic nets, schemata
KR and reasoning
„
„
syntax, semantics
derivation, entailment
Page 23 of 29
20-Apr-04
Lecture 1
54
KR Methods
‹ Production
Rules
‹ Trees, graphs (e.g. Semantic
networks, Concept maps, Concept
graphs, Ontology)
‹ Schemata and Frames
‹ Logic
=> Reasoning
Page 24 of 29
20-Apr-04
Lecture 1
55
Knowledge Representation
{
A good KR formalism must
z
z
z
z
z
{
Page 25 of 29
Make important things explicit
Facilitate the processing of knowledge
within the system
Be concise
Be transparent to human users and
facilitate machine/user interaction
Suppress unnecessary detail
The formalism we choose depends on
the application
Example 1
{
Consider the following three
representations of number 360
z
z
In its decimal form (360)
A vector of factors of 10
{
z
{
In its binary form
The three representations are
equivalent
z
z
Page 26 of 29
3x102+6x101+0*100
They have the same epistemological value
Which one we choose depends on the
given application
Example 1
{
If we want to answer queries of the
form “given an number, is it greater
than 1000?” the second representation
should be preferred
z
Page 27 of 29
The other two do not facilitate
computation, are not concise, and contain
a lot of unnecessary information
Example 2
{
Consider the following two representations
of knowledge “every plane has exactly two
pilots”
in FOL
z
{
z
In set theoretic notation
{
z
∀x plane(x) => (∃y ∃z pilot(y,x) ∧ pilot(z,x) ∧
¬equal(y,z) ∧ (∀w pilot(w,x) => equal(w,y) ∨
equal(w,z)))
cardinality(pilots(plane(x)) = 2
Which one is better?
Page 28 of 29
Example 2
{
The set theoretic representation is more
concise
z
z
z
Page 29 of 29
An obvious advantage is that it is easier to
modify
To state that “every plane has at most two
pilots” all we have to do is change = with ≤
It is not immediately obvious how to update
the FOL formula