HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS?

Can we representknowledge? HOW DOESHUMAN-LIKE KNOWLEDGECOME INTO BEING INARTIFICIAL ASSOCIATIVE SYSTEMS? Adrian Horzyk horzyk@agh.edu.pl AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY Faculty of Electrical Engineering, Automatics,Computer Science and Biomedical Engineering Department of Automatics and Biomedical Engineering Unit of Biocybernetics POLAND, 30-059 CRACOW, MICKIEWICZA AV. 30

Knowledgeallows to: • Rememberfacts, rules, objectsorclasses of them. • Consolidatevariousfacts and rulesaftertheirsimiliarities. • Associateobjects, facts, rules with contexts of theiroccurences. • Recallfacts and rulesusingcontext and associations. • Generalizeobjects, facts and rules. • Be creativeusinglearnedclasses of objects, facts and rules. HUMAN-LIKE KNOWLEDGE • Variousfacts and rulescan be associated and recalledthanks to: • Similaritiesof the data thatdefinethem. • Subsequencesof the data thatoccurinsidethem. Knowledge isactiveaggregation of data, facts and rulesthatcan be recalled and generalizedaccording to the context of theirrecalling. Human-likeknowledgecan be representedonly in reactivesystemsthatcanrepresentsuchnot redundantaggregations.

Knowledge: • Isnot a set of facts, rules, objectsorclasses of them. • Isnokindof a computermemoryor a database. • Doesnotremembereverythingprecisely. • Cannot be collectedalike data, facts and rules but itcan be formed for givenorcollected data, facts and rules. • Cannot be easytransferedfrom one system to anotheralike data, databases, facts and rules etc. Onlypieces of information, facts and rulescan be transferedintoanother system. Can be partiallytransferedthroughrecalledfacts and rules. • Isnotlimitedto any set of facts, rulesorobjectsbecausenew, creativeinputcontextscanlead to newfacts, rules, notices, observations and remarks on the basis of the same knowledge. WHAT IS NOT KNOWLEDGE ? Knowledge can be automaticallyformedonly in specialsystemsthatallow to activellyassociateand aggregatedata, factsand rules, and theirvariouscombinations and sequences.

Neuralassociativesystemsallows to: • Representvariousobjects, facts and rules in a unified form of data combinationsusingneurons. • Createclassesof representedobjectsafter most representativefeatures and theircombinations. • Triggerneuronsaccording to the context of otheractivatedneuronsorsensereceptors. • Use the contextof previouslyactivatedneuronsaccording to the timethathaselapsed from theiractivations. • Consolidate and combinevariousobjects, facts and rulesaftertheirsimiliarities and subsequences. • Associateobjects, facts, rules with contexts of theiroccurences. • Recallassociatedobjects, facts, rulesusingneworpreviouslyusedcontexts, questions etc. • Generalizeand evencreatenewobjects, facts and rules. NEURAL ASSOCIATIVE SYSTEMS

Artificialassociativesystems: • Model biologicalneuralassociativesystems, nervoussystems etc. • Defineassociative model of neurons(as-neurons)thatareable to reproducecontext and timedependencies of biologicalneurons. • Can be simulated, trained and adapted on today’scomputers. • Canusevarioustraining data setand evensetsof trainingsequences. • Canreproducetrainingsequencesorcreatenewones- be creative! • Cangeneralizeatvariouslevels: ARTIFICIALASSOCIATIVE SYSTEMS ArtificialAssociative Systems and AssociativeArtificialIntelligence (Polish) Sequencelevel Object level

Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

The externalexcitation of neuron E4 triggers the followingactivations of neurons: E4  E5  E2  E6 ASSOCIATIVE NEURAL GRAPH EVALUATION We gotsequence S2 as the answer for the externalexcitement of neuron E4:

Neuralassociativestructure for the linguisticobjects THE SIMPLE NEURAL STRUCTURE OF THE CONSECUTIVE LINGUISTIC OBJECTSrepresenting 7 sentences

Response to „Whatisknowledge?” • As-neuronsareconsecutivelyactivatedaftertrainingsequences and give the answers: • Knowledgeisfundamental for intelligence. • Knowledgeis not a set of facts and rules

Associative model of neurons • AS-NEURON: • Works in timethatiscrucial for allassociativeprocessesin the network of connected as-neurons. • Modelsrelaxation and refractionprocesses of biologicalneurons • Relaxation – continuousgradualreturning to itsrestingstate • Refraction – gradualreturning to itsrestingstateafteractivation • Optimizesitsactivityresponcesfor input data combinationschosingonlythe the most intensiveand frequentsubset of them. • Conditionallyplasticallychangesitssize, synaptictransmission and connections to other as-neurons. • Canrepresentmanysimilar as well as quitedifferentcombinations of inputstimuli (data). ASSOCIATIVE MODEL OF NEURONS

Knowledgecan be modelledusingartificialassociativesystems. • Training sequencescan be used to adaptartificialassociativesystems • Associativesystemssupplyus with ability to generalizeon variouslevels: • classescreated for objects • sequencesdescribingfacts and rules • Associativesystemscan be creativeaccording to the context, whichcanrecallnewassociations. CONCLUSION

? Theory of neural associativecomputationsand knowledge engineeringin the associativesystems Questions? Remarks? ArtificialAssociative Systems and AssociativeArtificialIntelligence (Polish) Google: Horzyk Adrian horzyk@agh.edu.pl

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HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS?