DOI: 10.17587/prin.15.485-496
Automata-based Model of Scientific Activity
V. I. Shelekhov, Head of Laboratory, vshel@iis.nsk.su,
A. P. Ershov Institute of Informatics Systems, Novosibirsk, 630090, Russian Federation
Corresponding author: Vladimir I. Shelekhov, Head of Laboratory, A. P. Ershov Institute of Informatics Systems, 630090, Novosibirsk, Russian Federation, E-mail: vshel@iis.nsk.su
Received on April 15, 2024
Accepted on July 23, 2024
The automata model of scientific activity is defined as an extension of the automata model of arbitrary activity. The automata model is built hierarchically and consists of a static part in the form of a set of data sections and a dynamic part defining a set of model processes. The structure of arbitrary activity is considered from the perspective of requirements engineering. To describe the structure of objects, a new data type is introduced: tree set. Tree sets of objects and activities are defined in the activity model. Science as a whole has a hierarchical tree structure and is defined as a tree set of scientific disciplines. The objects of the model of scientific activity are a theory and a set of scientific projects for each scientific discipline. The organisation of scientific activity in Russia is still inefficient. As an alternative, an organisation based on a portfolio of scientific projects formed by a team of leaders for each scientific discipline is considered. The team of leaders is created and updated in accordance with the principle of maximum competence. The apparatus of formal methods and requirements engineering is used to build a model of scientific activity. The static part of the model proposed by the author is formalised.
Keywords: science of science, automata-based engineering, control system, requirement engineering, system engineering, formal methods, ontology, agent-based model
pp. 485—496
For citation:
Shelekhov V. I. Automata-based Model of Scientific Activity, Programmnaya Ingeneria, 2024, vol. 15, no. 9, pp. 485—496. DOI: 10.17587/prin.15.485-496. (in Russian).
References:
- Shelekhov V. I., Tumurov E. G. Automata-based Software Engineering for Control System Design and Verification, Programmnaya Ingeneria, 2024, vol. 15, no. 2, pp. 73—86. DOI: 10.17587/prin.15.73-86 (in Russian).
- Shelekhov V. I., Tumurov E. G. Applying Automata-based Software Engineering for the Lift Control Program, Programmnaya Ingeneria, 2017, vol. 8, no. 4, pp. 99—111 DOI: 10.17587/prin.8.99-111 (in Russian).
- Shelekhov V. I. Automata-based Program Optimization by Applying Requirement Transformations, Programmnaya Ingeneria, 2015, no. 11, pp. 3—13 (in Russian).
- Shelekhov V. I. Automata-based software engineering: the language and development methods, Programmnaya Ingeneria, 2014, no. 4, pp. 3—15. DOI: 10.17587/prin.8.99-111 (in Russian).
- Levenchuk A. I. Systemic thinking: Textbook, Tollman, 2019, 534 p. (in Russian).
- Abrial J.-R. Modeling in Event-B: System and Software Engineering, Cambridge University Press, 2010, 586 p.
- Systems and software engineering — Life cycle processes — Requirements engineering. ISO/IEC/ IEEE 29148:2018, 2018.
- Shelekhov V. I. Program Classification in Software Engineering, Programmnaya Ingeneria, 2016, vol. 7, no. 12, pp. 531—538. DOI: 10.17587/prin.7.531-538 (in Russian).
- Hoare C. A. R. An axiomatic basis for computer programming, Communications of the ACM, 1969, vol. 12, no. 10, pp. 576— 585. DOI: 10.1145/363235.363259.
- Makarov V. L., Bakhtizin A. R. New toolkit in social sciences — agent-based models: general description and specific examples, Economics and Management, 2009, no. 12, pp. 13—25 (in Russian).
- Abramov V. I., Kudinov A. N., Evdokimov D. S. Application of social modelling using agent-based approach in the application to scientific and technological development, R&D implementation and maintenance of innovation potential, Vestnik VGUIT, 2019, vol. 81, no. 3, pp. 339—359. DOI: 10.20914/2310-1202-2019-3-339-359 (in Russian).
- Zagorulko Yu. A. Construction of scientific knowledge portals on the basis of ontology, ZhVT, 2007, vol. 12, no. 2, pp. 169—177 (in Russian).
- Zagorulko Yu. A., Borovikova O. I., Zagorulko G. B. Organisation of meaningful access to information resources on the basis of ontologies, Electronic libraries: promising methods and technologies, electronic collections, Proc. of 9th Russian Scientific Conference RCDL'2007, Pereslavl-Zalesskiy, 2007, vol. 1, pp. 217—224 (in Russian).
- Agent-Based Modeling in the Philosophy of Science, Stanford Encyclopedia of Philosophy, 2023, available at: https://plato.stanford. edu/entries/agent-modeling-philscience/ (date of access 25.06.2024).
- Wright S. The Roles of Mutation, Inbreeding, Crossbreeding, and Selection in Evolution, Sixth International Congress on Genetics, 1932, vol. 1, pp. 356—366.
- Devyatkin D., Nechaeva E., Suvorov R., Tikhomirov I. Formation of scientific landscape in the field of agricultural sciences, Foresight, vol. 12, no. 1, 2018, pp. 69—78. DOI: 10.17323/25002597.2018.1.69.78 (in Russian).
- Grimm V., Railsback S. F., Vincenot C. E. et al. The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism, Journal of Artificial Societies and Social Simulation, 2020, vol. 23, no. 2, article 7. DOI: 10.18564/jasss.4259.
- Tsioptsias N., Tako A., Robinson S. Model Validation and Testing in Simulation: a Literature Review, Open Access Series in Informatics (OASIcs), 2016, vol. 50, pp. 6:1—6:11.
- Niazi M., Hussain A., Kolberg M. Verification and Validation of Agent-Based Simulation using the VOMAS approach, Multi-Agent Systems and Simulation'09 (MASS '09), 2009, 7 p.
- Niazi M., Hussain A. A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments, IEEE Sensors Journal, 2011, vol. 11, no. 2, pp. 404—412. DOI: 10.1109/JSEN.2010.2068044.
- Kefalas P., Holcombe M., Eleftherakis G., Gheorghe M. A Formal Method for the Development of Agent-Based Systems, Intelligent Agent Software Engineering, 2003, pp. 68—98. DOI: 10.4018/978-1-59140-046-2.ch004.
- Jemni Ben Ayed L., Siala F. Event-B based Verification of Interaction Properties, Multi-Agent Systems, J. Softw., 2009, vol. 4, no. 4, pp. 357—364.
- Jarrar A., Ait Wakrime A., Balouki Y. Formal approach to model complex adaptive computing systems, Complex Adapt Syst Model., 2020, vol. 8, article 3, 35 p. DOI: 10.1186/s40294-020-0069-7.
- Parfenova S. L. Network model of scientific activity organization, Nauka. Innovations. Education, 2014, vol. 9, no. 2, pp. 78—89.
- OKVED — Russian Classifier of Economic Activities, 2022, available at: https://www.regfile.ru/okved2.html (date of access 25.06.2024).
- Ross T., Kardas M., Cucurull G., Scialom T., Hartshorn A., Saravia E., Poulton A., Kerkez V., Stojnic R. Galactica: A large language model for science. arXiv:2211.09085. 2022, 58 p.
- Krivoruchko V. V. On key actions for further reforming Russian science, Science Management: Theory and Practice, 2021, vol. 3, no. 4, pp. 36—43. DOI: 10.19181/smtp.2021.3.4.4.4
- Todosiychuk A. V. Science management in non-stationary economy, Science Studies, 2022, no. 3, pp. 71—85.
- Index of branches of science, Wikipedia, available at: https:// en.wikipedia.org/wiki/Index_of_branches_of_science (date of access 25.06.2024).
- Computing Curricula 2020: Paradigms for Global Computing Education, CC2020 Task Force Steering Committee; Gal-Ezer, Judith. Association for Computing Machinery (ACM) IEEE Computer Society (IEEE-CS), 2020.
- Categories within Computer Science, Cornell University, 2023, available at: https://arxiv.org/archive/cs (date of access 25.06.2024).
- Proydakov E. M. Tree of computer sciences, Naukovedcheskie issledovaniya, 2012, no. 2012, pp. 120—137.
- Sadovnichiy V. A., Vasenin V. A. Intellectual system of thematic research of scientometric data: prerequisites of creation and methodology of development. Part 1, Programmnaya Ingeneria, 2018, vol. 9, no. 2, pp. 51—58. DOI: 10.17587/prin.9.51-58.
- Kozitsyn A. S., Shachnev D. A. Towards creation of a system for determining the authority of conferences on the basis of scientometric data, Knowledge—Ontology—Theories (ZONT-23), Novosibirsk, IM SB RAS, 2023, pp. 160—168.
- Lewin K., Lippitt R., White R. Patterns of aggressive behavior in experimentally created social climates, The Journal of Social Psychology, 1939, pp. 271—301.
- Abakumova I. V., Elagina M. Y., Pronenko E. A., Nikonova D. Y. Semantic aspects of team interaction management, Young Researcher of Don, 2019, vol. 19, no. 4, pp. 121—126.