HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS? AGH UNIVERSITY OF...
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Transcript of HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS? AGH UNIVERSITY OF...
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 [email protected]
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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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, [email protected], 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 [email protected]
Theory of neuralassociative computations
and knowledge engineeringin the associative systems
Artificial Associative Systemsand
Associative Artificial Intelligence(Polish)
We can make our World a better place to live!