Emerging Complexity in Distributed Intelligent Systems.

Valentina Guleva,Klavdiya Bochenina,Alexander Alodjants,Egor Shikov,Alexander Boukhanovsky,Sergey Kovalchuk

doi:10.3390/e22121437

Valentina Guleva, Klavdiya Bochenina + Show 4 more

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https://doi.org/10.3390/e22121437

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Journal: Entropy	Publication Date: Dec 19, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: ITMO University

Abstract

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.

Highlights

The concept of distributed intelligence (DI) is currently known for decades [1]
Emergence of Complexity in Distributed intelligent systems (DIS) 4, DIS has an intrinsic connection with a notion of complexity, As we have shown in Section including such effects as critical transitions and nonlinearity, limited predictability, self-organization, As we have shown in Section 4, DIS has an intrinsic connection with a notion of complexity, and evolutionary dynamics
Some studies use problem statements with multiple agents to increase computational efficiency, while others explore the effects of network topology on learning performance

Summary

Introduction

The concept of distributed intelligence (DI) is currently known for decades [1]. Within the multitude of works and directions of DI, the distribution is applied to knowledge, information, action, and resources [2]. The DI solutions in many problem domains are growing in scale, achieving the support of big data volumes, ubiquitous computing resources, etc. This opens new opportunities and new behavior of DI. Considering artificial intelligence (AI) agents within the DAI approach, Entropy 2020, 22, 1437; doi:10.3390/e22121437 www.mdpi.com/journal/entropy

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Conclusion