Начиная с начала 2000 года осуществляется внедрение GHIS в здравоохранении, в рамках принятого проекта о реформирование информ


Mathematical Problems of Computer Science  49, 79--91, 2018. 

 
 

On  the Way to Dominating Cognition 
 

Edward M. Pogossian 
 

Institute for Informatics and Automation Problems of NASRA 
e-mail:epogossi@aua.am 

 
 
 

Abstract 
 

Humans become powered enough to question  the further types of their being 
in the universe but have not answers yet whether the solutions are in the 
corrections  of their genomes , discovering of new types of organizations of 
humans or in  transition to a new type of descends, humanoid machines or others. 

Assuming that scenarios with dominating cognition are the most promising to 
resolve the challenges of evolvement of humans and are the most expected for the 
next stage of their being, while the kernel of effective cognition is universal for all 
types of further being of humans, we present constructive models of the kernel, 
question the completeness of representations and ways of its further study. 

Keywords: Mental systems, Adequate, Constructive modeling, 
Cognition,Negentropics. 

 
 

1. Introduction 
 
1.1. 1. Following the cosmological views [33], the vast majority of realities we classify as 
entropics while interpreting Schrodinger [6] we classify as negentropics or negs, an island of not 
entropic realities r that can gain energy from others to preserve in space and time certain roots, 
i.e., realities, say, constituents or doings of r, determining the identity of r. 
Roots of negs, as it succeeds from definitions, have to include, at least, means to gain energy and 
ones to preserve those means that, in turn, require means to represent and affect realities, i.e., 
sensors and effectors, then, means to classify utilities, damagers and uncertains, i.e., classifiers 
of favorable, damaging or uncertain yet with respect to (wrt) the roots realities, as well as 
controllers ofthe doings. 
 1.2.1. We classify cellular realities, cellulars, as a type of negentropics that include alive cells in 
the roots able to reproduce and compose themselves into the organism while classifying humans 
as a type of cellulars. 
1.2.1. Mental doings of humans are backing the root and induced goals. They are either genomic 
or cognized in the life time while cognized mainly by acquisition from the cultures of 
communities. 

 
79 
 



On The Way to Dominating Cognition 
 

80 

Mental doings are represented by mental doers, mdoers, while mental classifying by mdoers or 
systems of mdoers, msystems (mss). 
Mdoers and mss, have, we assume, at least, IDs and can be processed consciously, particularly, 
for explaining and understanding doings/doers in communications.  
1.2.2. Mdoers include algorithms and are based, we assume, on and do over IDs of sensors, 
elaborate instructions for the effectors and are governed by the controllers. 
Mss induce systemic classifiers, say, Factories, Computers, Chess Positions. 
Mss, particularly, represent the goals, classify coexistence of nominated utilities/goals, i.e., 
represent relationships between them, can be processed to learn new utilities, to enhance 
effectiveness of mss or communicate them. 
1.2.3. Acquisition and accumulation of mss, as well as revelation of mss, particularly, by 
learning new utilities or by enhancement of effectiveness of ad hoc mss, comprise the balk of 
doings of a type of mss, cognizers.  
Processing of mss is governed, we assume, by controllers.   
For example, controllers explain mss “Humans” of the author by resolving it into this, ongoing 
text, namely, by corresponding English words to IDs of constituents of the mss. 
 That resolution, in general, could be started from any constituents of target mss, selected 
constituents, thus their IDs could be chained in a variety of modes and details depending, 
particularly, on the intensions of the author. 
1.2.4. A mighty way of enhancement of effectiveness of mss, and thus, cognizers, is the 
regularization of classifiers induced by mdoers and mss [47].   
Namely, classifiers Cl of members x of communities C are regularized in C if accompanied by 
ontological in C methods, instructions  allowing x regularly provide positive samples of inputs of 
Cl, as well as let the members of C do the same by communicating with x.  
In constructive regularization, those samples can be provided deterministically and without any 
involvement of cellulars while, otherwise, they can be grown up from a priory given prototypes 
like cells or crystals, be the products of services to humans or machines.   
1.3.  Accelerated mental power approaches humans  to “Homo Deus”  [34] with a chance of 
reliable preservation of cellular roots while, in parallel, to entire crashing of cellulars on the 
earth. 
The kernel of that strength is in the effectiveness of organizations of humans while the weakness 
is caused by the imperfectness of those organizations and their genomic heritage. 
Humans become powered enough to question the further types of their negentropic being in the 
universe but have not answers yet whether the solutions are in the corrections  of their genomes, 
discovering of new types of organizations of humans or in  transition to a new type of descends, 
humanoid machines or others. 
It is worth reminding that nowadays autonomous agents, a type of constructed negentropics, are 
challenging the cellular being of humans at all, as well as recall that following the Buddhist and 
Indian sources, humans being lemuroids or Atlantis in the far past could have not only cellular 
types of negentropicity [35].  
1.4. 1. In what follows, first, we present the modified specification of the models of mss, 
mentals, introduced in [47], refine systemic classifiers and constructive regularization of 
classifiers. 
We continue questioning the ways of proving that mentals can be adequate constructive models 
of mss. In addition to the approach in [47], based on examining  performances of particular mss 
with the corresponded them mentals, we analyze the ways to achieve consistency of 
performances of structural models of connectivity neuron nets, for example, artificial neuron 
nets, with purely functional models of mss, mentals. 
Assuming that the kernel of effective cognition is universal for all types of negs, thescenarios 
with dominating cognition are the most promising to resolve the challenges of evolvement of 



E.  Pogossian 81 

humans and are the most expected for the next stage of their being, finally, we question the 
completeness of representation of the kernel and ways of its further study. 
1.5. Our models are based on and try to fuse findings of many outstanding researchers. 
 We refer to some of their publications [1-35]  to study them in depth, as well as refer to some 
works [36]-[42], which can add to understanding of our ideas and their approbations [43]-[48]. 
 
 
2.Constructed Mental Systems 
 
2.1.1. Doers, in general, are, we assume, realities having in- out- put parts and for available 
inrealities, i.e., realities at the input parts, either elaborate certain output realities or stay passive. 
In- out- realities comprise their in- out- domains, or in- out-doms. 
Indoms wrt outputs are split into classes of equality, particularly, class of uncertainty (?) if 
inrealities don’t cause outputs. 
2.1.2. Doers are do-classifiersCl if indoms are split into two classes +Cl and ?Cl; otherwise they 
are corresponders, cors. 
Apparently, identifiers of do-classifiers Cl by themselves are sufficient to indicate their classes 
of equality, i.e., the positives +Cl,   while classes of cors can be indicated by pairing those 
identifiers with corresponding outputs. 
2.1.3. Doers of type of classifiers are sensors if inrealities are not necessarily pre-classified,  of 
type of cors are effectors if inrealities are necessarily classified while are controllers if both in- 
out- realities are necessarily classified. 
2.2.1.Realities I{i} are identifiers, IDs, of realities R{r} and Z{z}  wrt  Z if  
- to any r,z unique IDs i(r), i(z) are corresponded 
- to any r,z certain classifiers are linked allowing by IDs i to recall the corresponding r or z 
- any r can address any z to recall any r, z. 
Identified realities of given R, Z paired with their IDs are named nominals wrt Z.  

Z and R can coincide for, say, R presenting members of communities or their mdoers. 
 Controllers Cns, are assumed, can assign IDs to given mdoers aimed to control their processing 
and in- out- interactions with realities. 

Realities of Z can be interpreted as sets of Cns controlling in certain ways realities of R  
analogically to servers of “star” types controlling networks of computers  or, seemingly, 
analogically to  unicellular controllers. 
2.2.2. Nominals wrt Cns where realities of R are outputs of doers, particularly, sensors, 
controllers or effectors, are named otids while sets of otids of doers d are the alphabets of d. 
And sets of otids comprised from only some representatives of alphabets A1,A2, …,An  of doers 
d1,d2,…, dn are words in A1,A2, …,An. 
2.2.3. Classifiers of n-tuples of nominals are n-place relationships named rels for n=2. 
Rels (a,b) can be depended or not  on the orders of their arguments. 
2.3.1. Systems H over nominals Nls containing  rels Rls, i.e., Rls<Nls, or systems H over Nls/Rls,  
include Nls and if H’ are systems of H then systems of any subset of H’ linked to each other by 
rels of Rls and nominated by IDs consistent with nominations of Nls are systems of H as well. 
2.3.2. The totality H of systems over  Nls/Rls comprise Nls/Rls nets where nodes a, b are IDs 
corresponded  to systems of H,  the edges ( a,b ) are corresponded to rels between the systems 
represented by nodes a,b , signed, “colored “ by IDs of those rels and oriented from b to a if 
correspond to rels(a,b).  
Nls/Rls nets are, in fact, colored and oriented nets where nodes a depend on nodes b if rels (a,b) 
are corresponded to the edges linking a and b. 



On The Way to Dominating Cognition 
 

82 

Assuming Nls are 1st layer systems of Nls/Rls nets, the systems of n+1- layers are formed as 
systems over nominals of n-th layers and rels Rls.  
2.4.1.Given controllers Cns, effectors Efs and sensors Sns nominated wrt to Cns, or ces, doers D  
over IDs of ces, or doins or  cesdoins D, are doers D nominated wrt to Cns while indoms of D, 
Ffs, Cns are words in alphabets of the outputs of D, Cns, Sns. 
2.4.2. Bundles of otids of Sns,Cns and cesdoins at time t are t prints comprised into certain stores 
Pns and nominated wrt to Cns. 
 2.4.3.Basic cesnominals, or cesbns, include cesdoins D united with   Cns, Efs,Sns,Pns 
nominated wrtCns. 
2. 4.4. Systems of cesbnominals, or scesbns , are systems over Nls,Rls  where Nls are cesbns. 
The totalities of scesbns comprise cesnets Nts. 
2.5. The following types of scesbns  can equally correspond to algorithms, say in Markov, Java 
or other equal modes. 
Equal to rules by Markov are types of doins, regularities, or regs, corresponding certain otids to 
only some selected words of indoms. 

Algorithms are scesbns comprised from regs by rels similar to ones comprising rules into 
algorithms by Markov [29,30].  

Cessbns algorithms, in fact, detail ones by Markov with respect to detailing the origin of 
rules. 

Scesbns of the types of “abstract classes”, referring, say, to  ones in Java, are systems of 
algorithms, or methods, in rels of the types: “attributed”, “parented” and  “done by”, with other 
abstract classes  eventually, comprising  algorithms by Markov.  

Abstracts expand abstract classes by allowing arbitrary Rls  rels with other abstracts.  
Finally, packages of abstracts and their libraries are mimicking the ones in Java. 

2.6.1. While scesbns  can range from the sets of disjoined to the totally connected to each other 
systems we refine mental systems, mss,  as those of scesbns  that are connectivity subnets G of 
cesNts  rooted in the nodes a of Nts. 
Namely, total connectivity  scesbns G rooted at nodes a n (or total a connectivity scesbns G, or 
total connectivity a/ scesbns  G )of cesNts are connectivity subnets of Nts rooted in a. 
And a rooted scesbns  G’ of total connectivity a scesbns  G, or a mentals, or, generally, mentals, 
are a rooted connectivity subsystems of G. Then, a1,a2,.., an aspects of G’ are a1,a2,…, an 
rooted connectivity subsystems of G’. 

Apparently, connectivity a scesbns  are a mentals and nodes a1,a2,..,an by themselves can be 
the aspects of G’.  
The totalities of mentals of cesNts comprise cesthesauruses cesTh. 
2.6.2.Decompositions of 1st depth, or 1st decompositions, of a mentals G having nodes a at some 
layer k of cesNts are a1,a2,..,an rooted mentals of all subsystems G1,G2,…,Gn  of G with nodes 
a1,a2,…,an of k-1 layers of Nts connected to a. 

And if G1,..,Gn are i-th decomposition of G then i-1 th decomposition of G will be the union 
of 1st decompositions of G1,…,Gn. 
Apparently, the terminal decompositions of G will be comprised from bnominals of Nts. 
The unions of i-th decompositions of G for i=1,…,k-1 comprise total decompositions of G. 
2.6.3. Analogously can be  defined j-th abstractions and total abstractions of a mentals G in the 
way that  1st abstractions of a mentals G with nodes a at some layer k of cesnets Nts could be  
a1,a2,..,an rooted mentals G1,G2,…,Gn of all subsystems of G with a1,a2,…,an at the k+1 layers 
of Nts and connected to a, etc. 
2.7.1.Thesauruses cesTh are assumed to be stored by analogy with storing libraries, say in Java.  
Namely, nodes of cesnets are stored with IDs of the nodes, the classifiers of IDs of the nodes, 
IDs of rels of nodes a with nodes b along with IDs of those b. 



E.  Pogossian 83 

Nodes a corresponded to ces abstracts d, in addition, contain either the decision makers of d or 
the references to them.  
2.7.2. Apparently, nodes corresponded to ces abstracts of cesnets or in cesTh will coincide with 
abstract classes of Java in the case when their rels with other nodes of cmnets are restricted by 
“attributed”, “parented” and “done by” ones.  
2.7.3. Started fr identifying common  for communities C  doers, mdoers and msystems, so far, 
we have specified doins, scesbns, cesnets and cesTh thesauruses as well as  total connectivity 
scesbns, their connectivity subsystems, mentals, and the aspects of mentals representing, we 
assume, mss and the aspects of mss. 
To question whether mentals can be adequate constructive models for mss and for that aim refine 
constructive modeling and adequacy as it follows. 
 
 
3. SYSTEMIC   vs.  DO CLASSIFIERS  
 
3.1. Classifiers in their min mode identify some realities while if they are regularized, they can 
regularly provide samples of their indoms.  In turn, regularized classifiers can be models or 
adequate models of each other either constructive or not, as it follows. 
3.2.1. Do classifiers Cl were defined as a type of doers that for realities at the input parts either 
elaborate certain outputs or stay passive, thus, splitting indoms into two classes +Cland?Cl,  We 
assume that not only doers but any mss h induce certain systemic classifiers hsCl with positives 
+hsCl that  can be presented by mentals corresponded to mss as  follows. 
3.2.2.1. Realities r,r’ are (fuzzy) equal with respect to doins d if d is applied to r,r’ outputs 
(fuzzy) the same otids. In other words, d analyzing r, r’ by their embedded regs doesn’t find any 
(fuzzy) distinction between r and r’. 
Due to equality of realities, as a rule, they can be incomplete, approximate or fuzzy later on in 
refining equality we skip to name the option of their fuzziness.  
3.2.2.2. Doers are equal if for any inputs their outputs coincide. 
For constructiveness of that requirement, we assume that indoms of doers are made finite by 
some criteria.  
Thus, doers are equal if their performances, i.e., the pairs input/output, for inputs of their indoms 
coincide. 
3.2.2.3.Mentals G,G’ are equal if certain doers d determine that decompositions of G,G’untill 
terminal doins are isomorphic wrt equality of corresponded to each other doins while those doins 
are linked by the equal rels. 
3.3.Realities r match to mentals h if certain doers d can reveal in r equal to h systems h’. 
Thus, pairs (mentals h, d) determine systemic classifiers hsCl with positives +hsCl . 
 
 
4. Regularized and Modeled Classifiers  
 
4.1.1. Classifiers Cl of members x of communities C, x € C, are regularized  if  x can  
accompany Cl, first, by methods Clrgz letting x provide positives of +Cl regularly, and second,  
x by ontological in C communicativesClcms  can explain  Clrgz to any y € C  why y  can  
provide positives of +Cl. 
Regularized are, for example, classifiers of produced goods, grown up domestic plants and 
animals, services and skills provided hand to hand. 
Scientists x discovered some realities aand classified them by regularized classifiers Cl can by 
Clrgz provide samples of a and by Clcms successfully explain to others how to do the same.  



On The Way to Dominating Cognition 
 

84 

3.4.2.Classifiers Cl are fuzzy regularized  if they are regularized but  to the extent that outputs of 
Clrgz  only fuzzy match to +Cl and the fuzziness  can range up to not matching to +Cl. 
4.1.2.. Let’s recall that positives of +Cl are realities ofindoms of do classifiers doCland are 
systems of doers for systemic classifiers sCl. 
Particularly, if mss h are some do classifiers doCl positives matching +hsCl will be   
doClthemselveswhile positives of +doCl will be some realities of indoms of +doCl. 
4.1.3. Corollary1. Regularized mss h are equal to hrgz methods wrt out realities of hrgz, i.e., if 
realities rare reproducible by hrgz then they match h. 
Apparently, regularized mss h necessarily have to include systemic classifiers hsCL either 
explicitly or implicitly. 
Statements on mss, as a rule, have also transparent fuzzy interpretations why often can be 
skipped later.    
4.2.1.The samples of classified realities r can be constructed deterministically and totally 
independent  from cellulars in constructive regularization , conrgz, or can be regularized not 
constructively, particularly be provided not deterministically, be grown up from a priory given 
realities like cells or crystals, be a product of services to humans or machines.   
For example, mss Goods (Gds) representing producible in the frame of some civilization goods, 
saymss Computers (Cps) since are reproducible from totally not cellular realities. 
Conrgz are mss Algorithms (Ags), represented either as Turing Machines, Post Productions, 
Markov algorithms or Recursive Functions that can be specified to be assembled from not 
cellular units by mathematicians or programmers, and even more, they are enumerable. 
Mss Wheat, Domestic Animals are fuzzy regularized and grown up while mss Services of 
cellulars , say Treatments by Doctors, are fuzzy regularized and are inseparable from cellulars. 
Assuming a numerical scale for fuzziness of regularization its variable f might be zero for Cps, 
Gds and Ags,  range from zero  for mss Deterministic Methods (DM)  to  ½ for the Heuristics,  
and have f=1  for mss  Conscious, Emotions, Passions questioned yet to be regularized.  
4.2.2. Humans tend to regularized classifiers Cl` for the advantage not only passively classify but 
actively provide positives of classifiers. 
Constructive regularization let, in addition,  exempt positives from being cellulars, thus, 
expanding leverages to amplify target doings . Recall, for example, transition from riding by 
horses to cars or trains.  
4.3.1. Regularized mss h are modeling mss h* if out realities of hrgz match h*. 
Regularized mss h are adequately modeling mss h* if they are modeling h* and for any realities 
r* matching h* hrgz can produce realities r equal to r*. 
Apparently, regularized mss h are adequately modeling themselves. 
4.3.2.By Church DM can be adequately modeled by Ags, i.e., to any DM equal Ags can be 
corresponded. 
Recalling Corollary1, it can be stated the 
Corollary2. Regularized mss h are equal to certain algorithms wrt out realities matching h. 
4.3.3. Church thesis, we assume, can be expanded for fuzzy and not deterministic algorithms and 
methods as well.  
Namely, DM can be expanded to Methods (Mds) and Ags to fAgs adding to Ags, say, 
probabilistic, distributed and heuristic methods / algorithms. 
Corollary3. Regularized mss h (fuzzy or not) are equal to certain algorithms (fuzzy or not) wrt 
realities matching h. 
 
 
 
 



E.  Pogossian 85 

5. Consisting Functional and Connectivity Mental Models 
 
5.1. Adequacy of mss is questioned functionally and connectively.  
Functional questioning examines the equality of performances of mss and models of mss of any 
origin. 
In contrast, in connectivity modeling it is required that the units of the models mss have to be 
adequate models of the units of nerves systems, neurons, and, particularly, looking for adequacy 
of mss with artificial nets of neurons, ANN. 
Following the ideas of functional modeling, we examine our models of mss, mentals, by 
consistency of their performances with targeted mss, first of all cognitive ones, providing models 
of those mss aimed to support our  expectations, or the hypothesis Ham,  that mentals might be 
adequate models of mss [47].. 
So far, we have advanced in supporting Ham, particularly, by the models of representations and 
inductive learning [36,40-42], utilization of realities by acquisition or by strategies [38, 39,], 
matching to classifiers [44-46] and communications [47].  
5.2.1. The hypothesis Ham, apparently, induce the question to be studied, namely, whether 
thesauruses cesTh, i.e., thetotalities of mentals defined in [47]and above, nervy nets NN are 
equal  wrt to representation and performances of mss. 
5.2.2. These days adequacy of connectivity models of NN are intensively examined, specially, 
for artificial neuron nets, ANN, that are consistent with a variety of physiological classifiers of 
NN.  
Actually,  the question arises  about the hypothesis eANN?cesTh on equality of ANN and  the 
nets of mentals,  cesTh, or in other words, whether ANN can be organized in the way to be equal 
to NM with respect of representation and performance of mss? 
Our expectations for the positive answer to eANN?NM are supported by the following premises. 
5.3.1. Doers of cesTh, doins, and doers of ANN, neurons, both are doers over IDs of nominals 
over given sensors/effectors/controllers. 
5.3.2. Nets of neurons and their constituent neurons are classifiers either of types of sensors or of 
classifiers of IDs of nominals or n tuples of IDs of nominals, i.e., rels. 
In turn, doins of the types of do classifiers and rels, by definitions, are classifiers as well while 
the kernel of the corresponders (cors), i.e., regularities (regs), are not. .   
5.3.3. Complex doins of high degree layers of cesTh, i.e., mentals, induce incrementally growing 
up systems of classifiers and explicit rels between them. 
In turn, higher layers of neurons nets are systems of classifiers of lower ones with rels implicitly 
represented, seemingly, by synaptic links.  
5.3.4. ANN can, we assume, equally represent cesThregs, thus, any cors.  
Positive expectations follow, particularly, from interpretation of conditional or not reflexes by 
Pavlov via cors.  
For example, conditional reflex of secretion of saliva to food accompanied with lighting cause 
conditional reflex of the same secretion to the lighting without the food. 
 In other words, activation of neuron classifiers Cl of food accompanied with activation of 
classifiersCl’ of lighting generates positive synaptic links of the causal type between those 
classifiers. 
As a result, a time type casual rels “lighting causes food “between IDs of Cl and Cl’ is formed. 
And lighting will activate that rels rising expectations for the food that, apparently, can be 
eliminated if regularly fails. 
While rels “lighting cause food “is a do classifier it can be interpreted also as regs 
ifthe appearance of lighting  on the left  indicates that food can be expected, thus, if lighting then 
food cors can be processed in reasoning and algorithms. 



On The Way to Dominating Cognition 
 

86 

5.3.5. Recalling, that amount of synaptic links in NN of any human concurs with the amount of 
particles in universe it is worth to assume that neurons with synaptic links between them provide 
an example of constructive implementation of rels and cors. 
 4. To resolve completely the eANN?cesTh open, yet, state, at least, the following questions. 
5.4.1. Assuming ANN can equaly represent any cesTh classifier and cors whether they can 
represent cesTh algorithms, abstract classes and abstracts explicitly or not, and if explicitly then 
how the ANN have to be organized? 
5.4.2.How human- computer communications including explanations and understanding of mss, 
prognostication of utilities and search strategies having proper cesTh models can be interpreted 
in ANN? 
5.4.3.Whether the rels between IDs of nominals can be implemented in cesTh analogously to 
ones in NN, seemingly, realized by activation of synaptic links between target nominals? 
5.4.4. Eventually, how mss identified by psychologists can be equally interpreted both in ANN 
and cesTh? 
 
 
6. Challenging Cellular Negentropicity 
 
6.1. Evolutionary cellularswere always fighting and competing for energetic resources because 
any environment they chose to reproduce themselves is physically restricted by energy that, 
inevitably, causes competition and fighting for the survival. 
6.2.Mental power of humans lets them become a unique cellulars approaching the entire sources 
of energy of the Universe, thus, getting an opportunity to exempt fighting for the survival.  
6.3. 1.Picks of mental power of humans in the scales of its effectiveness and efficiency include, 
particularly, the following ones. 
6.3.2. Awesome coverage by classifiers of the entire types of realities of the Universe and 
enormously expansion of the amount of classifiers.                   
Classifications span from particles, black energy and matter to cosmic spaceships, from 
sequencing of genomes to gene engineering, from computers to global networks and their 
controllers. 
An estimate of the amount of human classifiers can be the number of about 300, 000.00 units in 
English dictionaries.  
6.3.3. Continuous rise of the abstractness and universality of classifiers. 
6.3.4. Approaching the regularization of classifiers of any external or internal realities. 
Starting from the grown up regularization, say plants and domestic animals, humans are 
intensively progressing in constructive regularization of mental doings, specially cognition, that 
exempting from cellar dependency enhances the dimensions of empowering.      
6.3.5. Invention of organizations effectively integrating humans for common goals, particularly 
for the cognizing.  
6.4. The above and other dimensions of mental power let question whether humans with 
unlimited access to the sources of energy can organize themselves into a type of negentropics 
that instead of fighting for energetic resources will prioritize their mental doings, say, cognizing 
of Universe, to become so knowledgeable that could, at least, serve their successful ongoing 
access to the energy and be ready to prevent possible troubles with it. 
6. 5.1. An unconquered obstacle to organizations of humans, particularly, to ones with 
domination of cognition, is in their evolutionary genomic heritage to fight for the superiority and 
subsequent possible aggressiveness, greediness, evilness, cruelness resulting in evasion of 
members of organizations from routine obligations or their erosion by corruption.  



E.  Pogossian 87 

To avoid routine work and servicing for humans we could assume that with the growth of 
intelligence of machines they would be able to take that burden while humans could concentrate 
on creative work and cognition. 
Unfortunately, those options rise new serious problems. 
With passing services to machines humans have to delegate them certain control that inevitably 
will result in malicious usage of that control [34]. 
Then, deepening of cognition assumes effective cognizing organizations that again meets the 
obstacles with human genomic nature while the transmission of cognition to the machines 
challenges the further being of humans, at least, as a type of cellulars. 
6.5.2.The questions arise   whether humans being enough mentally empowered can transit to new 
types ofnegentropics free from drastic aspects of heritage of humans and able to preserve the 
attained advances in serving energy.    
And whether those negentropicss with dominating cognition can be classified as a new type of 
descents of humans consistent with their further being or as ones conquering with them for the 
unconditional priority.  
Those fundamental, critically important questions stay open yet and hopefully can be resolved 
with deepening cognition of ourselves and universe.  
6. 6.1. Thus, acknowledging nowadays intractability of the problem DDPEN of doingsfor quasi 
eternal, durable preservation of energized negentropicityin the universe let’s focuson the 
studying of mental doings of humans that to certain extent are invariant wrtall the types of 
negentropics. 
Namely, let’s focus on the problem of identification of mental doings of negentropics necessary 
for all of them for preserving energized negentropicity from the challenges of environments. 
In other words, for the class ENs of energized negentropics including not only humans but all 
negentropics having sufficient technologies to access to quasi eternal sources of energy we can 
state the problem IMDE PEN of  identification of mental doings necessary for all negentropics of 
ENs  in preservation of energized negentropicity (PEN) and effectiveness of those doings in 
PEN.  
6.6.2. To analyze mental doings of negentropics in EN we, apparently, refer to human ones as 
the base. 
Namely, so far we have presented the modified specification of certain constructively regularized 
models (CRM) of mss, mentals, introduced in [47] for the class MS of mss, i.e.,the class MS 
representing entire mss, that, we assume, can be adequate CRM for human mental doers and 
systems, mdoers and mss. 
Mdoers and mss, have, we assume, at least, IDs and can be processed consciously, particularly, 
for explaining and understanding doings/doers in communications.  
Construction of mentals refer only to the existence of sensors, effectors and mental controllers of 
humans as well as, in general, to their goal oriented doing without addressing the peculiarities of 
goals of any type of negentropics. 
Thus, accepting that sensors, effectors and mental controllers are inevitable for any negentropic, 
we assume that CRM for humans, mentals, can be considered as CRM for all negentropics 
including ones of the class EN. 
6.6.3. The scales of effectiveness of mental doings wrt PEN are diversified and include the ones 
of coverage of classified realities, abstractness, universality and regularization of classifiers. 
Focusing on the scales of constructive regularization of mental doings, we aim to approach to 
solving the problem CRMD PEN. 
And as step to solving CRMD, it is reasonable to study cognitive doings, or constructive 
regularization ofcognition, as  the problem CRC PEN as well as  sub problems of CRC including 
regularization of mss representing inductive and deductive inferences, acquisition, accumulation,  
enhancement of effectiveness , controllers and  communications.  



On The Way to Dominating Cognition 
 

88 

In the frame of the above assumptions, we are going to continue to advance in adequacy of 
mentals for the constituents of cognizers started in [47] as well as continue to develop models of 
CRC in [36-46] 
6.7. To advance in CSC PEN we need to question the range of mental doings of humans 
inevitable for any cognizing negentropic of EN. 
Certain mental doings of humans are the subject of studying of cognition in AI in certain 
pragmatic human-machine contents but whether they cover the scope of ones inevitable for 
negentropics of EN with cognizers estranged from celulars. 
Whether motivation, will, intuition, consciousness, other dimensions of personality of humans 
have to be necessarily regularized? 
Mental patterns are consequent to the dimensions of doing of humans and include, particularly, 
the following types.   
1.Cognitive 
2.1.Everyday life :  adaptation to self-care, health and safety, social interactions and transactions 
at home, school and work , memorization of basic instructions, personal data (name, address) 
and important interests, goals setting and problem solving, judgments  as well as doing 
integrated, i.e., setting goals, making decisions then judgment of the consequences 
2.2. Highest 
3. Communitive 
4. Humanistic and ethical 
Thus, we need to mirror   mental patterns of human mss then target the ones inevitable for 
negentropics of EN.  
 
 
 7.  Conclusions  
 
7.1. We continue studying of adequate constructive models of mental systems. 
First, we present the modified specification of the models of mss, mentals, introduced in [47], 
refine systemic classifiers and constructive regularization of classifiers. 
Then, question the ways of proving that mentals can be adequate constructive models of mss and 
analyze the ways to achieve consistency of performances of structural models of connectivity 
neuron nets, for example, artificial neuron nets, with purely functional models of mss, mentals. 
Assuming that the kernel of effective cognition is universal for all types of negs, we assume that 
scenarios with dominating cognition are the most promising to resolve the challenges of 
evolvement of humans and are the most expected for the next stage of their being. 
7.2. We argue that mss, apparently, have to be prioritized with respect to the necessities of the 
most expected scenarios of human development and positioned in corresponding scales. 
Given expected scenario the patterns of mss have to be revealed all together approximating 
relevant to scenarios basic strata of human mss. 
 For that, we assume, the views, particularly, on the nature of cognition and mental healthiness 
by philosophers, psychiatrists and psychologists have to be recalled as well as the views on 
organizational consistency by personal managers and ones on the norms of humanistic and 
ethical behavior.   
Finally, from prioritized for given scenarios mss have to be excluded mss with ad hoc adequate 
constructive models to focus the research on the rest of the targets.  
 
 
 
 



E.  Pogossian 89 

References 
 
[1] R. Feynman, The Meaning of it All, Addison Wesley, Massachusetts, 1998. 
[2] J. von Neuman, Theory of Self-reproducing Automata, University of Illinois Press, 1966. 
[3]    T. Winograd and F. Flores, Understanding Computers and Cognition. A new foundation for 

design  Publishers, Huntington, NY, 1986.  
[4]    J. Flavell, The Developmental Psychology of Jean Piaget,  D.VanNostrand Comp. Inc., 

Princeton, N.J., 1962. 
[5] Z. Pylyshyn, “Seeing and Visualizing: It’s Not What You Think”, An Essay On Vision and 

Visual Imagination, http://ruccs.rutgers.edu/faculty/pylyshyn.html, 2004. 
[6] E. Shrodinger, Mind and Matter, Cambridge, 1956. 
[7] J. Mandler, The Foundations of Mind: Origins of Conceptual Thought, Oxford Univ. Press, 

2004. 
[8] A.Turing, Computing Machinery and Intelligence, Mind 49, 1950, [Reprinted in Minds and 

machines. A. Anderson (ed.), Engelwood Cliffs NJ, Prentice Hall, 1964].   
[9] N. Chomsky, Aspects of the Theory of Syntax, MIT Press, 1965. 
[10] J. Pitrat, “Consciousness and conscience, in artificial beings: the conscience of a conscious 

machine”, ISTE, London, UK, 2009. 
[11] K Fu, Syntactic Methods in Pattern Recognition, London, 1974.  
[12] E. Zermelo, “Ubereineanwendung der mengenlehre auf die theorie des Shachspiels”, 

Proceedings of the fifth International Conference of Mathematicians, Cambridge University 
Press, 1912, pp. 501-504, 

[13] J. Searle, “Is the brain’s mind a computer program?,Scientific American, vol.  262, pp. 26-31, 
1990. 

[14] D. Roy, “Grounding language in the world: signs, schemas, and meaning cognitive machines 
group”, The Media Laboratory, MIT http://www.media.mit.edu/cogmac / projects. html, pp. 1-
27, 2005.  

[15] C. Shannon, “Programming a computer for playing chess”, Philosophical Magazine Ser.7, vol. 
41, 1950. 

[16] Ch. Brutyan, I. Zaslavski, and L. Mkrtchyan, “On methods of automated synthesis of positional 
strategies in games”, ProblemiKibernetiki, Moscow, vol. 19, pp. 41-75, 1967. 

[17] M. Botvinnik, “About solving approximate problems”, S. Radio, Moscow, (in Russian), 1979.  
[18] M. Botvinnik, “Computers in chess: solving inexact  search problems”, Springer Series in 

Symbolic Computation, with Appendixes, Springer-Verlag:, New York, vol. ?, pp. ??, 1984. 
[19] A. Elo, The Rating of Chess Players, Past and Present, London, 1978. 
[20] J. Laird,The Soar Cognitive Architecture, MIT Press, England, 2012. 
[21] (2013) [Online]. Available: UNDL Foundation, www.unlweb.net. 
[22]  “UNL Specifications”, UNL Center of UNDL Foundation, 2005. 
[23] F. Gobet, “Chunking mechanisms in human learning”, Trends in Cognitive Sciences, vol. 5, pp. 

236-243, 2001. 
[24] G. Atkinson, Chess and Machine Intuition, Ablex Publishing Corporation, New Jersey, 1993. 
[25] J. Furnkranz, “Machine Learning in Games: A Survey in “Machines that Learn to Play 

Games”, Nova Sci., 2001. 
[26] J. Moon, Topics on Tournaments, N.Y., Holt, and Winston, 1968. 
[27] R. Benergi, Theory of Problem Solving,Mir, Moscow, 1972. 
[28] R. Penrose, The Emperor’s  New Mind, Oxford University Press, 1999. 
[29] A. Markov and N. Nagorni, Theory of Algorifms, Nauka, Moscow, 1984. 
[30] A. Maltzev, Algorithms and Recursive Functions, Nauka, Moscow, 1965. 

http://ruccs.rutgers.edu/faculty/pylyshyn.html
http://www.media.mit.edu/cogmac%20/
http://archive.computerhistory.org/projects/chess/related_materials/text/2-0%20and%202-1.Programming_a_computer_for_playing_chess.shannon/2-0%20and%202-1.Programming_a_computer_for_playing_chess.shannon.062303002.pdf


On The Way to Dominating Cognition 
 

90 

[31] H. Marandjian, “A method of synthesis of programs of numeric functions”, Mathematical 
Problems of Cybernetics and Computer, Yerevan,vol. 26, pp. 5-13, 1986. 

[32] F. Harary,  Graph Theory, Addison-Wesley, 1969. 
[33] Perlov D. VilenkinA.Cosmology for the Curious. Springer, 2017 
[34] Harari Y.N. Homo Deus: A Brief History of Tomorrow. Harper-Collins, 2017 
[35] Helena Blavatsky: Between Light and Darkness. Жизнь замечательных людей (inRussian). 

Moscow: Молодаягвардия, 2010 
[36] E.Pogossian and A. Martirosian . “Learning”, Reference book on Intellectual Systems. Radio 

i Svjas Publishing Company, Moscow, (in Russian), vol. 2, pp. 206-231, 1990.  
[37] E. Pogossian, “On modeling cognition”, Computer Science and Information Technologies 

(CSIT11), Yerevan, 2011, pp 194-198, 2011. 
[38] E. Pogossian, “Specifying personalized expertise.  International Association for Development 

of the Information Society (IADIS)”, International Conference Cognition and Exploratory 
Learning in Digital Age(CELDA 2006), Barcelona, Spain, 2006, pp. 151-159. 

[39] E. Pogossian, V. Vahradyan, and A. Grigoryan, “On competing agents consistent with expert 
knowledge”, Lecture Notes in Computer Science, AIS-ADM-07: The International Workshop 
on Autonomous Intelligent Systems - Agents and Data Mining, St. Petersburg, Russia, June 6-
7, 2007, pp. 229-241.  

[40] E. Pogossian, “On measures of performance of functions of human mind”, 6th International 
Conference in Computer Science and Information Technologies, CSIT2007, Yerevan, 2007, 
pp. 149-154. 

[41] E. Pogossian, “On a transparent presentation of written English syntax”, 5th Intern. Cognitive 
Linguistics Conference, VrijeUniversiteit, Amsterdam, July 1996, pp. 209-214. 

[42] E. Pogossian, “Adaptation of combinatorial algorithms”, Academy of Sci. of Armenia, (in 
Russian), p. 293, Yerevan, 1983. 

[43] E. Pogossian, “Effectiveness enhancing knowledge based strategies for SSRGT class of 
defense problems”, NATO ASI 2011 Prediction and Recognition of Piracy Efforts Using 
Collaborative Human-Centric Information Systems, Salamanca, Spain, 2011, pp. 16. 

[44] Z. Naghashyan and E. Pogossian, “Developing Java software for representation, acquisition 
and management of strategy knowledge", Mathematical Problems of Computer Sciences, 
Proc. of IIAP, Yerevan, pp.187-195,2010,. 

[45] K. Khachatryan and S. Grigoryan, “Java programs for matching situations to the meanings of 
SSRGT games”, Proceedings of SEUA Annual conference, Yerevan, Armenia, 2013, pp. 
135-141. 

[46] S. Grigoryan “Research and development of algorithms and programs of knowledge 
acquisition and their effective application to resistance problems”, PhD, p 111, Yerevan, 
Armenia, 2016. 

[47] E. Pogossian, “Towards adequate constructive models of mental systems”, 12th International 
Conference in Computer Science and Information Technologies, CSIT2017, Yerevan, 2017, pp. 
96-101,2017,  as well as IEEE's Xplore electronic library and is available at the link 
:http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8307363 

[48] E. Pogossian, S. Grigoryan and N. Hakobyan, “On Systemic Classification and Machine 
Learning”, 12th International Conference in Computer Science and Information 
Technologies, CSIT2017, Yerevan, 2017,pp.102-107. 

[49] S. Grigoryan, N. Hakobyan and  H. Vrtanesyan, “Object –oriented Modeling of Systemic 
Classifiers and Matching to Classifiers”, Mathematical Problems of Computer Sciences, 
Proc. of IIAP, Yerevan, pp.102-107,2018. 
 

Submitted  22.09.2017, accepted  12.02.2018. 

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8307363


E.  Pogossian 91 

Իմացության գերակայության Ճանապարհին 
 

Է.  Պողոսյան 
 

Ամփոփում 
 

    Մարդիկ դառնում են այնքան զորեղ, որ հարցականի տակ են դնում են իրենց լինե-
լիության հետագա ձևերը տիեզերքում, բայց նրանք դեռևս չեն գտել պատասխան՝ 
արդյո՞ք լուծումը իրենց գենոմի ճշգրտման մեջ է, մարդկանց տիպի նոր միավորներ  
բացահայտելու մեջ է, թե՞ նոր տեսակի ժառանգների ձևափոխման մեջ է՝ մարդանման 
մեքենաներ և այլն։ Ենթադրելով, որ իմացության գերակայությամբ սցենարները 
ամենա հեռանկարայինն են մարդկային զարգացման մարտահրավերներում և 
ամենա պահանջվածը նրանց գոյության հաջորդ փուլում, ինչպես նաև այն,  որ 
արդյունավետ իմացության միջուկը համընդհանուր է բոլոր տեսակի մարդկային 
ապագա գոյությունների համար, աշխատանքում  ներկայացնում ենք միջուկի կառու-
ցողական մոդելներ, հարցադրում դրանց նկարագրողական ամբողջա կա-նությունը և 
դրանց հետազոտման հետագա եղանակների ուղիները։" 
 
 
 

К доминированию познания 
 

Э. Погосян 
 

Аннотация 

    Знания и способности человека приближают его к возможности энергетически 
надежного сохранения своего существования, но параллельно  и к возможности  
полного уничтожения. Сила людей существенно определяется эффективностью их 
организаций, одновременно, их слабость обусловлена несовершенством этих же 
организаций, а также,   геномным наследием человека.  
Знания человека становятся достаточными, чтобы подвергнуть сомнению 

устойчивость клеточной  организации  бытия во вселенной , но пока не дают ответа, 
находятся ли решения в исправлении генома человека, обнаружении новых типов 
организации людей или в переходе к новому типу  человекомашинного развития.  
Мы считаем, что сценарии с доминирующим познанием являются наиболее 

перспективными для ответа на вышеуказанные проблемы и являются наиболее 
ожидаемыми для следующего этапа развития  человека. Полагая, что ядро 
эффективного познания является универсальным для всех типов негэнтропного бытия, 
мы сосредотачиваем исследования на извлечении и изучении этого ядра, в частности, 
предлагаем конструктивные модели ядра, обсуждаем полноту и вопросы  дальнейших 
исследований.