HOW DOESHUMAN-LIKE KNOWLEDGE

COME INTO BEING INARTIFICIAL ASSOCIATIVE SYSTEMS?

AGH UNIVERSITY OF SCIENCE AND TECHNOLOGYFaculty of Electrical Engineering, Automatics,

Computer Science and Biomedical EngineeringDepartment of Automatics and Biomedical Engineering

Unit of BiocyberneticsPOLAND, 30-059 CRACOW, MICKIEWICZA AV. 30

Adrian Horzykhorzyk@agh.edu.pl

Can we represent

knowledge?

HUMAN-LIKE KNOWLEDGEKnowledge allows to: Remember facts, rules, objects or classes of them. Consolidate various facts and rules after their similiarities. Associate objects, facts, rules with contexts of their occurences. Recall facts and rules using context and associations. Generalize objects, facts and rules. Be creative using learned classes of objects, facts and rules.Various facts and rules can be associated and recalled thanks to: Similarities of the data that define them. Subsequences of the data that occur inside them.

Knowledge is active aggregation of data, facts and rules that can be recalled and generalized according to the context of their recalling.

Human-like knowledge can be represented only in reactive systemsthat can represent such not redundant aggregations.

WHAT IS NOT KNOWLEDGE ? Knowledge: Is not a set of facts, rules, objects or classes of them. Is no kind of a computer memory or a database. Does not remember everything precisely. Cannot be collected alike data, facts and rules but it can be formed for

given or collected data, facts and rules. Cannot be easy transfered from one system to another alike data,

databases, facts and rules etc. Only pieces of information, facts and rules can be transfered into another system. Can be partially transfered through recalled facts and rules.

Is not limited to any set of facts, rules or objects because new, creative input contexts can lead to new facts, rules, notices, observations and remarks on the basis of the same knowledge.

Knowledge can be automatically formed only in special systems that allow to activelly associate and aggregate data, factsand rules, and their various combinations and sequences.

NEURAL ASSOCIATIVE SYSTEMSNeural associative systems allows to: Represent various objects, facts and rules in a unified form of

data combinations using neurons. Create classes of represented objects after most representative

features and their combinations. Trigger neurons according to the context of other activated

neurons or sense receptors. Use the context of previously activated neurons according to the

time that has elapsed from their activations. Consolidate and combine various objects, facts and rules after

their similiarities and subsequences. Associate objects, facts, rules with contexts of their occurences. Recall associated objects, facts, rules using new or previously

used contexts, questions etc. Generalize and even create new objects, facts and rules.

ARTIFICIALASSOCIATIVE SYSTEMS

Artificial associative systems: Model biological neural associative systems, nervous systems etc. Define associative model of neurons (as-neurons) that are able to

reproduce context and time dependencies of biological neurons. Can be simulated, trained and adapted on today’s computers. Can use various training data set

and even sets of training sequences. Can reproduce training sequences or

create new ones - be creative! Can generalize

at various levels:

Object level

Sequence level

Artificial Associative Systemsand

Associative Artificial Intelligence(Polish)

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

Transformation of database tables into associative structures

ASSORT creates the basis graph structure of associative systems.

Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

TABLE

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(0)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(1)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

E2:1

E1:1 E3:1d=1,00 | WE2,E3=1,00

d=0,50 | WE1,E3=0,67

d=1,00 | WE1,E2=1,00S1

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(2)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

S2

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:2

E1:1 E3:1

E4:1 E6:1

E5:1

d=0,33 | WE4,E6=0,50d=0,50 | WE5,E6=0,67

d=1,00 | WE2,E6=0,67

d=1,00 | WE2,E3=0,67

d=0,50 | WE1,E3=0,67

d=1,00 | WE4,E5=1,00

d=0,50 | WE4,E2=0,67

d=1,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(3)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

S3

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:3

E1:1 E3:1

E4:1 E6:1

E5:2

E8:1E7:1

d=0,50 | WE5,E8=0,40d=0,33 | WE7,E8=0,50

d=1,00 | WE2,E8=0,50

d=0,33 | WE4,E6=0,50d=0,50 | WE5,E6=0,40

d=1,00 | WE2,E6=0,50

d=1,00 | WE2,E3=0,50

d=0,50 | WE1,E3=0,67

d=1,00 | WE7,E5=1,00d=1,00 | WE4,E5=1,00

d=0,50 | WE4,E2=0,67d=0,50 | WE7,E2=0,67d=2,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(4)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

S4

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:3

E1:1 E3:1

E4:1 E6:1

E5:2

E8:2E7:2

E9:1d=0,50 | WE5,E8=0,40d=0,83 | WE7,E8=0,59d=1,00 | WE9,E8=1,00

d=1,00 | WE2,E8=0,50

d=0,33 | WE4,E6=0,50d=0,50 | WE5,E6=0,40

d=1,00 | WE2,E6=0,50

d=1,00 | WE2,E3=0,50

d=0,50 | WE1,E3=0,67

d=1,00 | WE7,E5=0,67

d=1,00 | WE7,E9=0,67

d=1,00 | WE4,E5=1,00

d=0,50 | WE4,E2=0,67d=0,50 | WE7,E2=0,40d=2,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(5)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

S5

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:4

E1:1 E3:2

E4:2 E6:1

E5:2

E8:2E7:2

E9:1d=0,50 | WE5,E8=0,40d=0,83 | WE7,E8=0,59d=1,00 | WE9,E8=1,00

d=1,00 | WE2,E8=0,40

d=0,33 | WE4,E6=0,29d=0,50 | WE5,E6=0,40

d=1,00 | WE2,E6=0,40

d=0,50 | WE4,E3=0,40d=2,00 | WE2,E3=0,67

d=0,50 | WE1,E3=0,67

d=1,00 | WE7,E5=0,67

d=1,00 | WE7,E9=0,67

d=1,00 | WE4,E5=0,67

d=1,50 | WE4,E2=0,86d=0,50 | WE7,E2=0,40d=2,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

for training sequences: S1, S2, S3, S4, S5

(6)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:9

E1:1 E3:3

E4:7 E6:5

E5:6

E8:4E7:4

E9:3d=0,50 | WE5,E8=0,15d=2,83 | WE7,E8=0,63d=3,00 | WE9,E8=1,00

d=1,00 | WE2,E8=0,20

d=1,67 | WE4,E6=0,38d=2,50 | WE5,E6=0,53

d=5,00 | WE2,E6=0,71

d=1,00 | WE4,E3=0,25d=3,00 | WE2,E3=0,50

d=0,50 | WE1,E3=0,67

d=1,00 | WE7,E5=0,29

d=3,00 | WE7,E9=0,86

d=5,00 | WE4,E5=0,83

d=4,50 | WE4,E2=0,78d=1,50 | WE7,E2=0,55d=6,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

S2

S4

S5

4x

2x

1x

ASSOCIATIVE NEURAL GRAPH EVALUATIONThe external excitation of neuron E4 triggers the following

activations of neurons: E4 E5 E2 E6

E4 E5S2 E2 E6

We got sequence S2 as the answer for the external excitement of neuron E4:

(7)

TRAINING SEQUENCES and their frequency of repetition in the training sequence set

ANAKG

E1 E2S1 E31x

E4 E5S2 E2 E65x

E7 E5S3 E2 E81x

E7 E9S4 E83x

E4 E2S5 E32x

E2:9

E1:1 E3:3

E4:7 E6:5

E5:6

E8:4E7:4

E9:3d=0,50 | WE5,E8=0,15d=2,83 | WE7,E8=0,63d=3,00 | WE9,E8=1,00

d=1,00 | WE2,E8=0,20

d=1,67 | WE4,E6=0,38d=2,50 | WE5,E6=0,53

d=5,00 | WE2,E6=0,71

d=1,00 | WE4,E3=0,25d=3,00 | WE2,E3=0,50

d=0,50 | WE1,E3=0,67

d=1,00 | WE7,E5=0,29

d=3,00 | WE7,E9=0,86

d=5,00 | WE4,E5=0,83

d=4,50 | WE4,E2=0,78d=1,50 | WE7,E2=0,55d=6,00 | WE5,E2=1,00

d=1,00 | WE1,E2=1,00

2x actively 1x aggregates

4x and

1x a

1x basis

1x be

1x being

1x associative

1x consolidates

1x can

1x comes

1x for

1x fundamental

5x facts

1x intelligence

1x into

2x is

1x in

1x not

7x knowledge

1x objects

3x of2x on

1x reacts

3x rules

1x set1x representation

2x represented

1x systems

3x the

2x various

7x . (full stop)

d=2,00w=0,44

d=1,00w=0,67

d=1,00w=1,00

d=1,00w=1,00

d=1,00w=1,00

d=3,00w=0,86

d=3,00w=0,75

d=2,00w=0,80

d=1,00w=1,00

d=1,00w=1,00

d=1,00w=1,00

d=1,00w=0,67

d=1,00w=1,00

d=1,00w=0,50

d=2,00w=1,00

d=1,00w=1,00

d=1,00w=1,00

d=1,00w=0,25

d=1,00w=1,00

d=3,00w=1,00

d=1,00w=1,00

d=1,00w=0,25

d=2,00w=1,00

d=1,00w=0,25

d=1,00w=0,67

d=1,00w=1,00

d=1,00w=0,50

d=1,00w=0,67

d=1,00w=1,00

d=1,00w=0,40

d=2,00w=0,57

d=0,33w=0,40

d=0,33w=0,40

d=0,33w=0,29

d=0,50w=0,40

d=0,33w=0,20

d=1,00w=0,50

d=0,33w=0,50

d=0,50w=0,67

d=1,00w=0,25

d=1,00w=1,00

d=1,00w=1,00

d=1,00w=0,67

d=1,00w=0,67

d=0,50w=0,67

d=0,50w=0,40

d=0,50w=0,13

d=1,00w=1,00

d=1,00w=1,00

d=0,50w=0,67

d=1,00w=1,00

d=1,00w=0,50

d=1,00w=1,00

d=1,00w=0,25

d=1,00w=1,00

d=1,00w=0,50

d=0,25w=0,40

d=0,33w=0,50

d=0,50w=0,13

d=0,50w=0,13

d=0,50w=0,67

d=0,50w=0,67

d=0,50w=0,67

7x START d=7,00w=1,00

d=7,00w=1,00

d=0,33w=0,50

d=0,33w=- 0,5

d=0,33w=0,50

THE SIMPLE NEURAL STRUCTURE OF THE

CONSECUTIVE LINGUISTIC OBJECTS

representing 7 sentences

Neural associative structure for the linguistic objects

Response to „What is knowledge?”

As-neurons are consecutively activated after training sequences and give the answers: Knowledge is fundamental for intelligence. Knowledge is not a set of facts and rules

ASSOCIATIVE MODEL OF NEURONS

Associative model of neurons

AS-NEURON:

Works in time that is crucial for all associative processesin the network of connected as-neurons.

Models relaxation and refraction processes of biological neurons Relaxation – continuous gradual returning to its resting state

Refraction – gradual returning to its resting state after activation

Optimizes its activity responces for input data combinations chosing only the the most intensive and frequent subset of them.

Conditionally plastically changes its size, synaptic transmission and connections to other as-neurons.

Can represent many similar as well as quite different combinations of input stimuli (data).

CONCLUSION Knowledge can be modelled using artificial

associative systems. Training sequences can be used to adapt artificial

associative systems Associative systems supply us with ability to

generalize on various levels: classes created for objects sequences describing facts and rules

Associative systems can be creative according to the context, which can recall new associations.

?Questions? Remarks?

Google: Horzyk Adrianhorzyk@agh.edu.pl

Theory of neuralassociative computations

and knowledge engineeringin the associative systems

Artificial Associative Systemsand

Associative Artificial Intelligence(Polish)

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