how does human-like knowledge come into being in artificial associative systems?
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Can we represent knowledge ?. HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS?. Adrian Horzyk [email protected]. AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering - PowerPoint PPT PresentationTRANSCRIPT
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HOW DOESHUMAN-LIKE KNOWLEDGECOME INTO BEING INARTIFICIAL ASSOCIATIVE SYSTEMS?AGH UNIVERSITY OF SCIENCE AND TECHNOLOGYFaculty of Electrical Engineering, Automatics,Computer Science and Biomedical EngineeringDepartment of Automatics and Biomedical EngineeringUnit of BiocyberneticsPOLAND, 30-059 CRACOW, MICKIEWICZA AV. 30
Adrian [email protected] 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 SYSTEMSArtificial 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 todays computers.Can use various training data setand even sets of training sequences.Can reproduce training sequences orcreate new ones - be creative!Can generalizeat various levels:
Object levelSequence levelArtificial Associative SystemsandAssociative Artificial Intelligence(Polish)
Transformation of database tables into associative structuresASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and Technology
TABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLETransformation of database tables into associative structures
ASSORT creates the basis graph structure of associative systems.Adrian Horzyk, [email protected], AGH University of Science and TechnologyTABLEASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH CONSTRUCTIONfor training sequences: S1, S2, S3, S4, S5
ASSOCIATIVE NEURAL GRAPH EVALUATIONThe external excitation of neuron E4 triggers the following activations of neurons: E4 E5 E2 E6
We got sequence S2 as the answer for the external excitement of neuron E4:
THE SIMPLE NEURAL STRUCTURE OF THE CONSECUTIVE LINGUISTIC OBJECTSrepresenting 7 sentencesNeural associative structure for the linguistic objects25Response 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 rulesASSOCIATIVE MODEL OF NEURONSAssociative model of neuronsAS-NEURON:Works in time that is crucial for all associative processesin the network of connected as-neurons.Models relaxation and refraction processes of biological neuronsRelaxation continuous gradual returning to its resting stateRefraction gradual returning to its resting state after activationOptimizes 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).
CONCLUSIONKnowledge can be modelled using artificial associative systems.Training sequences can be used to adapt artificial associative systemsAssociative systems supply us with ability to generalize on various levels:classes created for objectssequences describing facts and rulesAssociative systems can be creative according to the context, which can recall new associations.
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