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Applied Sciences
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Intelligence in Natural and Digital Computing
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Intelligence in Natural and Digital Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 9023

Special Issue Editor

Prof. Dr. Song-Ju Kim

E-Mail Website
Guest Editor
1. SOBIN Institute LLC, 3-38-7 Keyakizaka, Kawanishi, Hyogo 666-0145, Japan
2. Department of Electrical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-Ku, Tokyo 125-8585, Japan
Interests: natural intelligence; artificial intelligence; natural computing; dynamical systems; randomness

Special Issue Information

Dear Colleagues,

In recent years, there has been a great deal of research into artificial intelligence (AI) and machine learning (ML). Initially limited to applications specialising in image recognition and feature extraction, the scope has gradually expanded to include applications in the creation of art, such as music and painting. However, the underlying ideas of AI are inspired by and modelled after natural phenomena, including the brain and biological systems, and in this sense the models cannot be universal and each model has its own limitations.

In the study of complex systems, when we modelled parallel computation in the brain, we identified the functional expression of ‘emergence’ as a property that makes it difficult to reduce the results of computation to a one-dimensional sequential explanation. Even in the brain, there are still properties that we humans have not fully grasped, and there must be an abundance of completely new computational principles and intelligent phenomena that are directly rooted in other properties inherent to natural phenomena, such as fluctuations, conservation laws, and continuity.

This Special Issue focuses on computational principles rooted in the unique properties of natural phenomena (parallelism, fluctuations, conservation laws, and continuity) and their applications in science and engineering. This includes the discovery and identification of intelligent phenomena, which can be regarded as ‘intelligence’ or part of it, and their scientific and engineering applications in a wide range of phenomena, including not only physical phenomena such as climate and environmental sciences, but also biological, human behavioural phenomena.

Blockchain technology, which has revolutionised the world of digital computation through the use of randomness and parallel distributed processing, has also recently increased its social status and importance. Intelligence in digital computing, such as AI, ML, IoT and other ICT systems, including blockchain applications are also welcome.

Potential topics include, but are not limited to:

  • Artificial intelligence;
  • Machine learning;
  • Natural computing;
  • Reinforcement learning;
  • Decision making;
  • IoT and/or ICT systems;
  • Blockchain technology;
  • Complex systems;
  • Modelling of biological systems;
  • Complexity;
  • Computation theory;
  • Dynamical systems;
  • Nonlinear systems;
  • Non-equilibrium statistical physics;
  • Earth and environmental science;
  • Communications;
  • Human behaviour.

Prof. Dr. Song-Ju Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • natural computing
  • reinforcement learning
  • decision making
  • IoT and/or ICT systems
  • blockchain technology
  • complex systems
  • modelling of biological systems
  • complexity
  • computation theory
  • dynamical systems
  • nonlinear systems
  • non-equilibrium statistical physics
  • earth and environmental science
  • communications
  • human behaviour

Published Papers (3 papers)

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Research

17 pages, 1079 KiB  
Article
Human Randomness in the Rock-Paper-Scissors Game
by Takahiro Komai, Hiroaki Kurokawa and Song-Ju Kim
Appl. Sci. 2022, 12(23), 12192; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312192 - 28 Nov 2022
Viewed by 3631
Abstract
In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS [...] Read more.
In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series. Full article
(This article belongs to the Special Issue Intelligence in Natural and Digital Computing)
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19 pages, 1500 KiB  
Article
Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks
by So Hasegawa, Ryoma Kitagawa, Aohan Li, Song-Ju Kim, Yoshito Watanabe, Yozo Shoji and Mikio Hasegawa
Appl. Sci. 2022, 12(15), 7424; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157424 - 24 Jul 2022
Cited by 5 | Viewed by 2045
Abstract
In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method [...] Read more.
In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods. Full article
(This article belongs to the Special Issue Intelligence in Natural and Digital Computing)
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11 pages, 12558 KiB  
Article
A Binary Decision Model and Fat Tails in Financial Market
by Kazuo Sano
Appl. Sci. 2022, 12(14), 7019; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147019 - 12 Jul 2022
Cited by 2 | Viewed by 1486
Abstract
Binary decision models have been the subject of renewed research in recent years. In these models, agents follow a stochastic evolution where they must choose between two possible choices by taking into account the choices of their peers. Kirman explained the process of [...] Read more.
Binary decision models have been the subject of renewed research in recent years. In these models, agents follow a stochastic evolution where they must choose between two possible choices by taking into account the choices of their peers. Kirman explained the process of ant social herding using a simple model, and he conducted an interesting simulation. The fat-tail distribution in the security market is well known, but its causes have not been sufficiently clarified. The aim of this article is to clarify them by a very simple model. In this article, by establishing a simple security market model and by applying the model of Kirman, the fat tail observed for price fluctuations is reproduced. Recent research in neuroscience has shown that noise plays a positive roll and enables us to have a deeper understanding of a natural commonality between ants and traders. The beauty competition of Keynes is kept in mind, and it is shown that a cause of the fat tail is the balance between independence and interdependence of the economic agents. Using a natural computing algorithm called Kirman’s ant model, I conducted a time series analysis of finance that appears when simplifying the human “behavior of imitating others”. The results show that natural fat tails appear. Full article
(This article belongs to the Special Issue Intelligence in Natural and Digital Computing)
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