Computational and Cognitive Networks Department
Head of Department:
Phone: (+37410) 28-20-80, (+37460) 270020
Head of Direction Vladimir Sahakyan
Mathematical Modelling of Distributed Systems
Head of Direction Eduard Poghosyan
Cognitive Algorithms and Models
“Cognitive Algorithms and Models” and Theory of Algorithms Directions in IIAP, led by Edward Pogossian since 1973 and Hrant Marandjian since 1975, correspondingly, were branched from the Laboratory of Mathematical Logics and Constructive Mathematics led by Igor Zaslavski since 1963 in the noble traditions of the school by Andrey Markov, one of the founders of Computer Sciences along with Turing, Church and Post.
AI studies were initiated at IIAP since its foundation in 1957 by an outstanding mathematician Sergey Mergelyan.
1. Cognitive Modeling, or Artificial Intelligence research direction, interpreting Alonzo Church, and then Allan Turing, is a branch of science aimed at understanding humans by providing adequate constructive cognitive models, at least, comparable in effectiveness to
mental doings of humans.
2. Mental doings are a variety of doings collaborative with communities, based on the classification of realities as favourable or damaging, with respect to our utilities, to support our being in the Universe.
3. Cognizing, following the comprehensive psychological models by Jean Piaget are mental doings on learning and organizing mental systems (mss) mostly reducible to a few fundamental rules of revelation and acquisition of mental systems accumulated in communities.
4. We aim to advance in
- refining human ways of cognizing the Universe
- specifying adequate constructive models of cognizing
- revealing constraints on cognizing
- alternating means of cognizing the Universe.
5. At this stage we focus on constructing adequate models of learnable by Piaget cognizers able to advance from certain root cognitive doings to the highest ones.
Head of Direction
Systems of Remote Monitoring and Control
Head of Direction Tigran Shahinyan
Dynamical Systems and Networks
The dynamical systems and networks, defined by maps or differential equations, are considered. The problems on mathematical modeling of vision and memory mechanism in some of such networks are studied; the pattern formation mechanisms and signal processing are analyzed. The randomness modeling, by means of sequences of pseudorandom numbers, is studied. Some problems on uniform distribution, Markov chains, and independent random sequences, are analyzed; here, some of possible applications are the tests of randomness and evolutionary algorithms. By the machine learning method, some statements on neural activity are considered.
Head of Direction Artur Petrosyan
Network and Cloud Services
- Pogossian E., Constructing Models of Being by Ccognizing, Monograph, The date of publication To Be Determined.
We introduce conceptual frameworks for OJP and RGT problems/Solvers that are detailed and deepen in below listed 9 sections.
Solvers in General
- 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.
- E. Pogossian, V. Vahradyan, A. Grigoryan. On Competing Agents Consistent with Expert Knowledge", Lecture Notes in Computer Science, AIS-ADM-07: The Intern. Workshop on Autonomous Intelligent Systems - Agents and Data Mining, June 5 -7, 2007, St. Petersburg, p.11.
- 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.
- Pogossian E. On Measurable Models of Promotion of Negentropic Strategies by Cognition, New Trends in Information Technologies, ITHEA, Sofia, 2010, pp.161-168.
- Grigoryan S., Hakobyan N. and Baghdasaryan T., Knowledge-Based Solvers for RGT Combinatorial Problems, 13th International Conference in Computer Science and Information Technologies, CSIT2019, Yerevan, 2019, pp36-46, also reprinted in IEEE's Xplorehttps: //ieeexplore.ieee.org/xpl/conhome/8890749/proceeding.
- Hakobyan N., “A System for Transforming Images to Symbolic Presentation for Combinatorial Defense and Competition Problems”, ISSN 0002-306X. Proc. of the RA NAS and NPUA Ser. of tech. sc. 2019. V. LXXII, N2, pp. 199-209.
Developing OJP Methodology
- Pogossian E. Management Strategy Search and Assessment Programming. CSIT99, Yerevan, August 1999.
- Danielyan E., Pogossian E. On Expanding of Method of Local Measurement to Evaluation of Management Strategies, Proceedings of SEUA, 2009.
- Pogossian E., On Performance Measures of Functions of Human Mind.
- Pogossian E., On Assessment of Performance of Systems by Combining On-the-Job and Expert Attributes Scales International Conference in Computer Sciences and Information Technologies, Yerevan, Armenia, 2015 pp. 331-335.
- Berberyan L. Modular Tool for Regulating and Analyzing Activities in Chess, Mathematical Problems of Computer Sciences, 47,2017.
Solvers in Management
- Danielyan E., Pogossian E., "An Application of Voting Methods to Strategy Assessment", Proceedings of the Conference on Computer Science and Information Technologies (CSIT'99), Yerevan, Armenia, 1999.
- Baghdasaryan T., Danielyan E, Pogossian E. Testing Oligopoly Strategy Plans by Their on the Job Performance Simulation. //Proceedings of the International Conference CSIT2005, Yerevan, Armenia, 2005.
Solvers in Intrusion Protection
- Pogossian E.: Combinatorial Game Models for Security Systems. NATO ARW on "Security and Embedded Systems", Porto Rio, Patras, Greece, Aug. (2005) 8-18
- E. Pogossian, A. Javadyan and E. Ivanyan., "Effective Discovery of Intrusion Protection Strategies.," The International Workshop on Agents and Data Mining, Lecture Notes in Computer Science, St. Petersburg, Russia, pp. 263-274, 2005.
Solvers in Defense
- E. Pogossian, D. Dionne, A. Grigoryan, J. Couture and E. Shahbazian, "Developing Goals Directed Search Models Empowering Strategies Against Single Ownership Air Threats," International Conference in Computer Sciences and Information Technologies, pp. 155-163, Yerevan, 2009.
- Arakelova E. and Pogossian E., "Tools for testing and correction of the completeness of knowledge acquisition by autistic children," pp. 159-165, Yerevan, Armenia, 2011.
- Pogossian E, Grigoryan S. and Berberyan L., Personalized Interactive Tutoring in Chess, 2016.
The Repository of Chess Classifiers
- Hambartsumyan M., Harutunyan Y., Pogossian E. The Repository of Units of Chess Vocabulary Ordered by Complexity of their Interpretations, National Academy of Sciences of Armenia, IPIA, 1974-1980 research reports.
On Background of RGT Solvers, OJP and Cognizers
- Pogossian E. (1985) Adaptation of Combinatorial Classifiers and Controllers, DrSci, Computing Center of Academy of Sciences, Moscow, defense thesis.
- B.K. Karapetyan, ОБ ОДНОЙ УНИФИКАЦИИ ПРЕДСТАВЛЕНИЯ ШАХМАТНЫХ ПОНЯТИЙ, Mathematical Problems of Computer Sciences, 1986, pp. 181-193.
- B.K. Karapetyan, ЯЗЫК ВЫСОКОГО УРОВНЯ ДЛЯ ОПИСАНИЯ СТРАТЕГИИ В ИГРАХ, Mathematical Problems of Computer Sciences, 1986, pp. 167-183.
On Background of Empowering Cognizers by Tournments
- Pogossian E. Systems of sets with minimum number of subsets and application to the theory of testing”. PhD, Computing Center of Academy of Sciences, Moscow, defense thesis, 1970.