See All News
Eduard Poghosyan
Position: Head of Direction: Cognitive Algorithms and Models
U00068AJZQ3O personal code/sci.grants

Phone: +374 (33 or 77) 263101

  Titles, Degree

Professor, PhD and Doctoral in Mathematical Cybernetics

Doctoral degree in Mathematical Cybernetics, Moscow, Computing Centre of the USSR Academy of Sciences, 1985; Thesis: Adaptation of combinatorial algorithms of classification and control. 
MBA program at American University of Armenia (AUA), 1992-94
PhD. in Mathematical Cybernetics, Moscow, Computing Centre of the USSR Academy of Sciences, 1970; Thesis: Systems of Non-comparable Sets with Minimum Number of Subsets and their Applications to Testing (complete solution of Shroder’s problem)
M.S. in Computer Science, Electro technical State University, St. Petersburg, 1966; Thesis: Multi Layer Neuron Network

  Professional Experience

Leader, Cognitive Modeling Research Direction at the Academy of Sciences of Armenia (from1973)
Teaching and PhD/MSc supervision in the field of Artificial Intelligence at leading universities of Armenia -Yerevan State, Engineering, American, Russian (1970-2014); from 2014, teaching and supervising at the  Academy of Sciences of Armenia
Strategy Management/ Marketing:  learned, taught and modelled at American University of Armenia (1994-98)

Member of Association of Cognitive Modeling (from 2010)
Member of ABSEL and US Marketing Associations (from 1995)
Representative of Armenia:  AI Union of USSR (1970-1992)
  Research Interests

  1. I research human cognizing with the aim of constructing and applying its adequate models, to reveal constraints on cognizing and its origination, approaching to understanding the origination of cellular realities.
  2. Two ongoing projects Constructing Adequate Models of Cognizing  and Constructing Adequate Models of Origination of Cognizing aim to examine viability of models of cognizing and its origination based on combinatorial games.
The projects are based on the following key assumptions and research findings:

A1. Cognitive systems and means of their construction are various compositions of basic 1-/2- place classifiers.
Only a few means are sufficient to realize those constructions and compositions. We argue A1 by providing decompositions of ongoing constructive cognitive models to the basic classifiers and interpreting in our models the essentials formulated by Jean Piaget asserting that only a few rules are responsible for the development of our cognizing.

A2. At present, the highest cognitive power of humans brings them close to the constructive modeling of their own self-reproduction, both biologically and cognitively.
A2 is based on references to current advances in chemical modeling of biological cells and AI advances in cognitive modeling.

A3. Information can originate in Nature.
A type of cognizers, octaves, composed from constellations  of basic 1-/2- place classifiers and able to enhancing the power of cognizing, but so far limited in that, can adequately model cognitive development of newborns by Piaget.

A4. Since a variety of single classifiers exist in Nature, the existence or origination of recurrent classifiers in Nature is not excluded.
For example, any atom represent single, not recurrent classifier, i.e. one able to identify certain realities lonely, solitarily, and form certain molecules.

A1-4 imply the following corollaries:

Clr1 from A1, A3, A4:  Classifiers, cognitive systems and means of their construction can originate in Nature within the frame of the laws of physics.

Clr2 from A1 and Clr1: Non-cellular, constructive cognizers comparable with the highest human ones can originate in Nature.

Clr3 from A2 and Clr3: In Nature, non-cellular, constructive cognizers can produce, in a variety of ways, descendant cognizers of comparable effectiveness. It is not excluded that existing cellulars, in fact, represent one of those constructed, evolved cognizers.

Cellular are much more complex than, say computers or satellites, which is why their appearance by chance has almost zero probability. At the same time, there are premises to constructive modeling of highest cognizers that, as it was stated in the assumption A2, are able to constructive modeling of their own self-reproduction both biologically and cognitively. Particularly, able to produce cellular cognizers developing themselves to the present day highest human level.

Clr4: If corollary Clr3 takes place, and if conditions similar to those around us (e.g. in our galaxy) are manifold in the Universe, it can be assumed that powerful cognizers can originate in various regions of the Universe and self-develop to the highest levels allowing them to reproduce themselves in a variety of modes.

Our cognitive models are consistent and complimentary to those in AI, allow to interpret communication, i.e., explaining and understanding of communicates of languages represented both by IDs of meanings of realities and by the samples of input domains of meanings. 
  Selected Publications
  1. E. Pogossian, “Specifying Adequate Models of Cognizers”, AIP Conference Proceedings 2757, 010001 (2023) doi:,[Online]. Available:
  2. E. Pogossian, Promoting Origination of Constituents  of Non Cellular Cognizers, Proceedings of CSIT2023, pp.65-68,,
  3. Pogossian, “Constructing Models of Being by Cognizing”, Academy of Sciences of Armenia, p. 496, Yerevan, 2020.
  4. E. Pogossian, “Adaptation of combinatorial algorithms”, Academy of Sciences of Armenia, p. 293, Yerevan, 1983.
  5. E. Pogossian, “ Systems of Non-comparable Sets with Minimum Number of Subsets and their Applications to Testing”,  PhD thesis in Mathematical Cybernetics, Moscow, Computing Centre of the Academy of Sciences of Russia, 1970 (also in the Journal of Combinatorial Analysis. Moscow State University, v. 4, 16-21 and in the Math. Problems of Cybernetics and Comp. Science, Yerevan, v. XVI, (1986) 148-161).
On February 27, 2024 at 14:00 the general seminar of IIAP will be held (the seminar's room, IV floor).

On the agenda
 there is the preliminary discussion of the dissertation for the PhD degree (specialty 05.13.05) of Tigran Galstyan on "Հատկանիշներ համապատասխանեցնող արտապատկերումների հայտնաբերման խնդրի վիճակագրական և հաշվողական բարդությունը".

  • 0014, Yerevan, Republic of Armenia, 1, P. Sevak str.
  • Phone: (+37410) 28-20-50
  • E-mail: