Russian Federation
Russian Federation
Abstract. In the context of the rapid development of science and technology, traditional methods are no longer effective, which requires more intelligent and adaptive solutions for information management and processing. The article explores and analyzes multi-agent systems (MAS), their role and importance in the field of innovation. Objective: systematization of technological solutions used in the development of multi-agent systems in innovation activities, as well as assessment of their impact on the effectiveness of innovation processes. Result and novelty: an analysis of architectural and algorithmic approaches to MAC development was carried out, modern software and hardware solutions, including the Internet of Things (IoT) and cloud platforms, were studied. Innovative aspects in the integration of MAC with physical devices are highlighted, which expands their functionality. Practical significance: the considered technological solutions support the practical application of MAC in various fields: from project management to optimization of research processes. The implementation of the IAU contributes to improved coordination and allocation of resources, which is especially important in conditions of high dynamics and uncertainty of the innovation environment. Discussion: It is emphasized that MAC development faces the challenges of complex architecture and computational costs. The importance of choosing a suitable development platform (for example, JADE, SPADE, NetLogo) is highlighted, depending on the needs of the project. The role of cloud technologies and IoT in the future development of multi-agent systems is emphasized.
multi-agent systems, distributed systems, intelligent agents, cloud technologies, Internet of Things, machine learning, visualization, automation, artificial intelligence
1. Bellifemine F., Caire G., Greenwood D. Developing Multi-Agent Systems with JADE. Chichester: Wiley, 2007. 304 p.
2. Russell S. J., Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Hoboken (NJ): Pearson, 2020. 1136 p.
3. Wooldridge M. J. An Introduction to MultiAgent Systems. 2nd ed. Chichester: Wiley, 2009. 488 p.
4. Bordini R. H., Hübner J. F., Wooldridge M. Programming Multi-Agent Systems in AgentSpeak Using Jason. Chichester: Wiley, 2007. 304 p.
5. Abramov V. I., Evdokimov D. S. Razrabotka kompleksa agent-orientirovannyh modeley sistemy gosudarstvennyh zakupok stran Evraziyskogo kontinenta // Problemy teorii i praktiki upravleniya. 2019. № 1. S. 15–23.
6. Diagnostika neustoychivyh kognitivnyh sostoyaniy aktivnyh agentov / P. P. D'yachuk [i dr.] // Neyroinformatika-2016: sbornik trudov XVIII Vserossiyskoy nauchno-tehnicheskoy konferencii (Moskva, 25–29 aprelya 2016 g.): v 3 ch. Ch. 1. M.: NIYaU MIFI, 2016. S. 259–270.
7. Komp'yuternoe situacionnoe modelirovanie v upravlenii ekonomikoy / V. L. Makarov [i dr.] // Gosudarstvennyy audit. Pravo. Ekonomika. 2017. № 3–4. S. 31–40.
8. R&D Subsidization Effect and Network Centralization: Evidence from an Agent-Based Micro-Policy Simulation / P. Angelini [et al.] // Journal of Artificial Societies and Social Simulation. 2017. Vol. 20, iss. 4. Art. No. 4. 22 p. DOI:https://doi.org/10.18564/jasss.3494
9. Gladkov L. A., Gladkova N. V. Vozmozhnosti upravleniya znaniyami na osnove gibridnyh intellektual'nyh metodov // Otkrytoe obrazovanie. 2013. № 6(101). C. 60–64.
10. The Peer Review Game: An Agent-Based Model of Scientists Facing Resource Constraints and Institutional Pressures / F. Bianchi [et al.]. 2018. Vol. 116, iss. 3. P. 1401–1420. DOI:https://doi.org/10.1007/s11192-018-2825-4
11. Ispol'zovanie mul'tiagentnyh tehnologiy dlya prognozirovaniya i identifikacii processa obucheniya / E. A. Nazoykin [i dr.] // Pischevaya promyshlennost'. 2015. № 6. S. 36–39.
12. Neves F., Campos P., Silva S. Innovation and Employment: An Agent-Based Approach // Journal of Artificial Societies and Social Simulation. 2019. Vol. 22, iss. 1. Art. No. 8. 32 p. DOI:https://doi.org/10.18564/jasss.3933
