Emerging Complexity in Distributed Intelligent Systems
Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificia...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/12/1437 |
| _version_ | 1827699726837874688 |
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| author | Valentina Guleva Egor Shikov Klavdiya Bochenina Sergey Kovalchuk Alexander Alodjants Alexander Boukhanovsky |
| author_facet | Valentina Guleva Egor Shikov Klavdiya Bochenina Sergey Kovalchuk Alexander Alodjants Alexander Boukhanovsky |
| author_sort | Valentina Guleva |
| collection | DOAJ |
| description | Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions. |
| first_indexed | 2024-03-10T13:55:38Z |
| format | Article |
| id | doaj.art-df0d3c7afbd0403db24cd6a72dae48f1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:55:38Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-df0d3c7afbd0403db24cd6a72dae48f12023-11-21T01:41:31ZengMDPI AGEntropy1099-43002020-12-012212143710.3390/e22121437Emerging Complexity in Distributed Intelligent SystemsValentina Guleva0Egor Shikov1Klavdiya Bochenina2Sergey Kovalchuk3Alexander Alodjants4Alexander Boukhanovsky5National Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaNational Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaNational Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaNational Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaNational Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaNational Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, RussiaDistributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.https://www.mdpi.com/1099-4300/22/12/1437distributed intelligent systemscomplex systemsnatural intelligence agentartificial intelligence agentsmultiagent systemsself-organization |
| spellingShingle | Valentina Guleva Egor Shikov Klavdiya Bochenina Sergey Kovalchuk Alexander Alodjants Alexander Boukhanovsky Emerging Complexity in Distributed Intelligent Systems Entropy distributed intelligent systems complex systems natural intelligence agent artificial intelligence agents multiagent systems self-organization |
| title | Emerging Complexity in Distributed Intelligent Systems |
| title_full | Emerging Complexity in Distributed Intelligent Systems |
| title_fullStr | Emerging Complexity in Distributed Intelligent Systems |
| title_full_unstemmed | Emerging Complexity in Distributed Intelligent Systems |
| title_short | Emerging Complexity in Distributed Intelligent Systems |
| title_sort | emerging complexity in distributed intelligent systems |
| topic | distributed intelligent systems complex systems natural intelligence agent artificial intelligence agents multiagent systems self-organization |
| url | https://www.mdpi.com/1099-4300/22/12/1437 |
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