Long-Term Adaptivity in Distributed Intelligent Systems: Study of ViaBots in a Simulated Environment

Ardavs, Arturs; Pudāne, Māra; Lavendelis, Egons; Ņikitenko, Agris

doi:10.3390/robotics8020025

Cited by 2 publications

(1 citation statement)

References 30 publications

(24 reference statements)

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“…The mechanisms of coordination and cooperation of agents as a society are fundamental characteristics of any MAS [ 40 ]. For example, the authors of [ 41 ] proposed a long-term adaptive distributed intelligent systems model that combines an organization theory and multiagent paradigm—ViaBots. Each part of the simulated system within ViaBots is designed as an autonomous agent.…”

Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

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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.

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Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

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Quantum-inspired modeling of distributed intelligence systems with artificial intelligent agents self-organization

Alodjants,

Tsarev,

Avdyushina

et al. 2024

Sci Rep

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Distributed intelligence systems (DIS) containing natural and artificial intelligence agents (NIA and AIA) for decision making (DM) belong to promising interdisciplinary studies aimed at digitalization of routine processes in industry, economy, management, and everyday life. In this work, we suggest a novel quantum-inspired approach to investigate the crucial features of DIS consisting of NIAs (users) and AIAs (digital assistants, or avatars). We suppose that N users and their avatars are located in N nodes of a complex avatar - avatar network. The avatars can receive information from and transmit it to each other within this network, while the users obtain information from the outside. The users are associated with their digital assistants and cannot communicate with each other directly. Depending on the meaningfulness/uselessness of the information presented by avatars, users show their attitude making emotional binary “like”/“dislike” responses. To characterize NIA cognitive abilities in a simple DM process, we propose a mapping procedure for the Russell’s valence-arousal circumplex model onto an effective quantum-like two-level system. The DIS aims to maximize the average satisfaction of users via AIAs long-term adaptation to their users. In this regard, we examine the opinion formation and social impact as a result of the collective emotional state evolution occurring in the DIS. We show that generalized cooperativity parameters $$G_i$$ G i , $$i=1,\dots,N$$ i = 1 , ⋯ , N introduced in this work play a significant role in DIS features reflecting the users activity in possible cooperation and responses to their avatar suggestions. These parameters reveal how frequently AIAs and NIAs communicate with each other accounting the cognitive abilities of NIAs and information losses within the network. We demonstrate that conditions for opinion formation and social impact in the DIS are relevant to the second-order non-equilibrium phase transition. The transition establishes a non-vanishing average information field inherent to information diffusion and long-term avatar adaptation to their users. It occurs above the phase transition threshold, i.e. at $$G_i>1$$ G i > 1 , which implies small (residual) social polarization of the NIAs community. Below the threshold, at weak AIA–NIA coupling ($$G_i\le 1$$ G i ≤ 1 ), many uncertainties in the DIS inhibit opinion formation and social impact for the DM agents due to the information diffusion suppression; the AIAs self-organization within the avatar–avatar network is elucidated in this limit. To increase the users’ impact, we suggest an adaptive approach by establishing a network-dependent coupling rate with their digital assistants. In this case, the mechanism of AIA control helps resolve the DM process in the presence of some uncertainties resulting from the variety of users’ preferences. Our findings open new perspectives in different areas where AIAs become effective teammates for humans to solve common routine problems in network organizations.

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Long-Term Adaptivity in Distributed Intelligent Systems: Study of ViaBots in a Simulated Environment

Cited by 2 publications

References 30 publications

Emerging Complexity in Distributed Intelligent Systems

Emerging Complexity in Distributed Intelligent Systems

Quantum-inspired modeling of distributed intelligence systems with artificial intelligent agents self-organization

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