Principles of Information Science
Chapter 6
Information Processing & Cognition,
Knowledge Theory
1,Information Processing
Definition of Information processing,
All operations exerted to the information itself for its
better utilization,
Information processing is necessary because most of
the information in its original form may not be good
for use,Thus information processing is the basis of
later operations,
Two categories of information processing,
1) Shallow Level of Information Processing
2) Deeper Level of Information Processing
Shallow Level of Information Processing
1,Processing for better operation,
information transformation & expression
2,Processing for better transferring,
communication & exchange
3,Processing for better maintaining,
record & storage
4,Processing for better sharing,
copying & reproduction
5,Processing for better retrieval,
classification,ordering,indexing
Deeper Level of Information Processing
1,Processing for higher efficiency,
compression based on syntax and semantics
2,Processing for improving noise immunity,
error correction based on syntax & semantics
3,Processing for purifying,
recognition & filtering
4,Processing for security improving
cryptography based on syntax & semantics
5,Processing for better utility,
prediction,search,inference,computation
Information Cognition
The purpose of information cognition is to produce
knowledge from information refining,
Cognition Information Knowledge
Information,The state & its varying manner,
Knowledge,The states & their varying laws,
2,Knowledge Theory
Classification of Knowledge
Formal Knowledge --
deals with the morphological relation of the object
Content Knowledge --
deals with the logical relation
Utility Knowledge --
deals with the value relation to the subject
Knowledge can only be the product of epistemological
information,
Representation of Knowledge
The elements of representation,
-- States
-- Manner of the states varying
States,
-- Numbering & description
Manner,
-- Deterministic,functions,equations
-- Random,probability,stochastic process
-- Fuzzy,grade of membership
-- Chaotic,non-linear differential equation
Measures of Knowledge
Unit Defining -- alt (奥特 )
X
X
X
1
2
1/2
1/2
The amount of a standard
unit problem --
the amount of a balanced
alternative problem,
The relation between alt and bit,
bit -- information unit
alt -- knowledge unit
Formal Knowledge
Descriptive Parameters
Certainty,C = {c | n (1,N)}
Probability (p )
Possibility (q )
Fuzziness (m )
Content Knowledge
Logic Truth,T = {t | n ? (1,N)}
n ?
n
n
n
n
Utility Knowledge
Utility,U = {u |n ? (1,N)} n
z = (a c )·(b t ) Integrative truth
Integrative Parameters
n n n
x = (ac )·(bt )·(gu ) Integrative utility n n n n
In simplest cases,
z = ac bt n n n
x = ac bt gu n n n n
General Measures
K(C,C*; R) = K(C*; R) - K(C; R) alt
K(C; R) = log [M(C)/M(C )] 0
M(C) = ? (c ) c n n n=1 N
K(T,T*; R) = K(T*; R) - K(T; R)
K(U,U*; R) = K(U*; R) - K(U; R)
K(x,x*; R) = K(x*; R) - K(x; R)
Mechanism of Knowledge Production
(1) Formal Knowledge Production,Induction
Step1 Given sample x(1),properly assign feature f(1);
Step2 Given sample x(2),check with f(1),keep x(2) if
f(2) = f(1); ignore x(2) if f(2) ? f(1);
Step3 Given sample x(n),do the same as step 2;
Step4 When n is sufficiently large,there may exists an
integer k<n such that k samples have the same
feature f(1) = · · · = f(k) = f(x);
Step5 The k samples having f(x) as their features form
a special set,a concept,Here f(x) is its intension
and all the samples constitute its extension,
(2) Utility Knowledge Production,Calculation
Step1 Define a goal,G;
Step2 when a stimulus,x,is received,remember its
morphological information,m;
Step3 Try to find whether it is helpful to reach the
goal,cos(x,G) < ± 90? If is,it has a positive
utility,or negative;
Step4 Establish the linkage between the morphology
and its utility,m ? u;
Step5 Whenever a new stimulus with the similar
morphological feature appears again,the
utility can then be understood,
o
(3) Content Knowledge Production,Deduction
Deduction
Formal
Knowledge
Utility
Knowledge
Content
Knowledge
The simplest deduction is to link the inputs with,do,
have,be,...”,For example,
“The stimulus with the form produces such a utility”,
3,Consciousness and Consciousness Machine
Definitions of Consciousness,
-- Philosophical Level (in wide sense)
The opposite part to matter,
-- Cognitive Level (in between the other two)
From awareness to intelligence,
-- Psychiatric Level (in narrow sense)
Awareness,
Level 1 Level 2 Level 3
Information
Awareness Cognition
Intelligence
The World
The Relations,Awareness,Cognition,Intelligence
Emergent Mechanism of Consciousness
Induction
Calculation
Deduction
Memory (Knowledge Bases)
Sensing
Sensing
Synt
Prag
Form
Util
Cont
Chapter 6
Information Processing & Cognition,
Knowledge Theory
1,Information Processing
Definition of Information processing,
All operations exerted to the information itself for its
better utilization,
Information processing is necessary because most of
the information in its original form may not be good
for use,Thus information processing is the basis of
later operations,
Two categories of information processing,
1) Shallow Level of Information Processing
2) Deeper Level of Information Processing
Shallow Level of Information Processing
1,Processing for better operation,
information transformation & expression
2,Processing for better transferring,
communication & exchange
3,Processing for better maintaining,
record & storage
4,Processing for better sharing,
copying & reproduction
5,Processing for better retrieval,
classification,ordering,indexing
Deeper Level of Information Processing
1,Processing for higher efficiency,
compression based on syntax and semantics
2,Processing for improving noise immunity,
error correction based on syntax & semantics
3,Processing for purifying,
recognition & filtering
4,Processing for security improving
cryptography based on syntax & semantics
5,Processing for better utility,
prediction,search,inference,computation
Information Cognition
The purpose of information cognition is to produce
knowledge from information refining,
Cognition Information Knowledge
Information,The state & its varying manner,
Knowledge,The states & their varying laws,
2,Knowledge Theory
Classification of Knowledge
Formal Knowledge --
deals with the morphological relation of the object
Content Knowledge --
deals with the logical relation
Utility Knowledge --
deals with the value relation to the subject
Knowledge can only be the product of epistemological
information,
Representation of Knowledge
The elements of representation,
-- States
-- Manner of the states varying
States,
-- Numbering & description
Manner,
-- Deterministic,functions,equations
-- Random,probability,stochastic process
-- Fuzzy,grade of membership
-- Chaotic,non-linear differential equation
Measures of Knowledge
Unit Defining -- alt (奥特 )
X
X
X
1
2
1/2
1/2
The amount of a standard
unit problem --
the amount of a balanced
alternative problem,
The relation between alt and bit,
bit -- information unit
alt -- knowledge unit
Formal Knowledge
Descriptive Parameters
Certainty,C = {c | n (1,N)}
Probability (p )
Possibility (q )
Fuzziness (m )
Content Knowledge
Logic Truth,T = {t | n ? (1,N)}
n ?
n
n
n
n
Utility Knowledge
Utility,U = {u |n ? (1,N)} n
z = (a c )·(b t ) Integrative truth
Integrative Parameters
n n n
x = (ac )·(bt )·(gu ) Integrative utility n n n n
In simplest cases,
z = ac bt n n n
x = ac bt gu n n n n
General Measures
K(C,C*; R) = K(C*; R) - K(C; R) alt
K(C; R) = log [M(C)/M(C )] 0
M(C) = ? (c ) c n n n=1 N
K(T,T*; R) = K(T*; R) - K(T; R)
K(U,U*; R) = K(U*; R) - K(U; R)
K(x,x*; R) = K(x*; R) - K(x; R)
Mechanism of Knowledge Production
(1) Formal Knowledge Production,Induction
Step1 Given sample x(1),properly assign feature f(1);
Step2 Given sample x(2),check with f(1),keep x(2) if
f(2) = f(1); ignore x(2) if f(2) ? f(1);
Step3 Given sample x(n),do the same as step 2;
Step4 When n is sufficiently large,there may exists an
integer k<n such that k samples have the same
feature f(1) = · · · = f(k) = f(x);
Step5 The k samples having f(x) as their features form
a special set,a concept,Here f(x) is its intension
and all the samples constitute its extension,
(2) Utility Knowledge Production,Calculation
Step1 Define a goal,G;
Step2 when a stimulus,x,is received,remember its
morphological information,m;
Step3 Try to find whether it is helpful to reach the
goal,cos(x,G) < ± 90? If is,it has a positive
utility,or negative;
Step4 Establish the linkage between the morphology
and its utility,m ? u;
Step5 Whenever a new stimulus with the similar
morphological feature appears again,the
utility can then be understood,
o
(3) Content Knowledge Production,Deduction
Deduction
Formal
Knowledge
Utility
Knowledge
Content
Knowledge
The simplest deduction is to link the inputs with,do,
have,be,...”,For example,
“The stimulus with the form produces such a utility”,
3,Consciousness and Consciousness Machine
Definitions of Consciousness,
-- Philosophical Level (in wide sense)
The opposite part to matter,
-- Cognitive Level (in between the other two)
From awareness to intelligence,
-- Psychiatric Level (in narrow sense)
Awareness,
Level 1 Level 2 Level 3
Information
Awareness Cognition
Intelligence
The World
The Relations,Awareness,Cognition,Intelligence
Emergent Mechanism of Consciousness
Induction
Calculation
Deduction
Memory (Knowledge Bases)
Sensing
Sensing
Synt
Prag
Form
Util
Cont
