Design of Technology for Prediction and Control System Based on Artificial Immune Systems and the Multi-agent Platform JADE

The article discusses current issues related to the development of an information optical Smart technology for distance learning of Honeywell's distributed Experion PKS control system for the oil and gas industry. About 70 % of industrial accidents are caused by the human factor through the fault of operators. The work of operators consists in monitoring and managing high-tech proc-esses through mnemonic scheme circuits and is characterized by increased tension in the visual apparatus, as well as general fatigue and loss of concentration. The innovative personalized tech-nology of distance learning takes into account the peculiarities of students' vision by adjusting the color supply of educational material and the dynamic presentation of information depending on the person's psychotype and is based on the use of cognitive, optical, multi-agent technologies, as well as ontological and immuno-network approaches. The development of cognitive mnemonic schemes is carried out taking into account these features, which allows one to reduce the load on the visual apparatus and increase the effectiveness of teaching practical skills when working with mnemonic schemes. An artificial immune systems approach is used to predict and evaluate the learning process and promptly adjust the knowledge obtaining process. A modified algorithm for the functioning of a distance learning system based on the use of optimization algorithms for arti-ficial intelligence and an algorithm for immuno-network modeling has been developed. General principles of creating mimic diagrams and existing Honeywell mnemonic schemes are considered. An example of the implementation of the proposed remote technology is presented and results of the simulation of cognitive mnemonic scheme for various categories of students with special needs are discussed.

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An artificial immune intelligent maintenance system for distributed industrial environments

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18769208 ◽

2018 ◽

Vol 232 (4) ◽

pp. 401-414

Author(s):

Luca Fasanotti ◽

Sergio Cavalieri ◽

Emanuele Dovere ◽

Paolo Gaiardelli ◽

Carlos E Pereira

Keyword(s):

Wastewater Treatment Plants ◽

Artificial Immune Systems ◽

Industrial Applications ◽

Training Dataset ◽

Multi Agent System ◽

Diagnostic Systems ◽

Artificial Immune ◽

Agent System ◽

Immune Systems ◽

Multi Agent

Maintenance services of geographically dispersed industrial applications, such as oil transfer systems via pipelines and wastewater treatment plants, are affected by high logistics costs and risks of permanent downtimes. The increasing availability of smart technologies and devices has led to the introduction of advanced prognostic and diagnostic systems to support maintenance activities. In this context, artificial immune systems support the development of industrial applications, where machines and equipment are capable of self-repairing, healing and learning due to their ability to learn from experience. However, the applicability of artificial immune systems has a limited set of contexts along with a low incidence of real-word implementations in the literature, and thus, additional explorative studies are necessary. This article describes a proposed hybrid system conceived by integrating a multi-agent system–based architecture with the main features of artificial immune systems and evaluates its potential applications in two different industrial settings. The flexibility of the behaviour of artificial immune systems methodologies allows for the implementation of a reliable diagnostic and prognostic system, while the choice of multi-agent system architecture enables a mix of autonomy and distributed processing that overcomes the strong limitations of a reduced training dataset.

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Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

Theoretical Biology and Medical Modelling ◽

10.1186/s12976-020-00130-x ◽

2020 ◽

Vol 17 (1) ◽

Author(s):

Galina Samigulina ◽

Zarina Samigulina

Keyword(s):

Artificial Immune Systems ◽

Artificial Immune ◽

Smart System ◽

Immune Systems ◽

Multi Agent ◽

Ontological Model

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Applications of Artificial Immune Systems in Agents

Handbook of Research on Artificial Immune Systems and Natural Computing ◽

10.4018/978-1-60566-310-4.ch005 ◽

2011 ◽

pp. 99-122

Author(s):

Luis Fernando Niño Vasquez ◽

Fredy Fernando Muñoz Mopan ◽

Camilo Eduardo Prieto Salazar ◽

José Guillermo Guarnizo Marín

Keyword(s):

Immune Response ◽

Clonal Selection ◽

Artificial Immune Systems ◽

Multi Agent Systems ◽

Artificial Immune ◽

Immune Systems ◽

Agent Systems ◽

Adaptive Immune ◽

Multi Agent ◽

Object Transportation

Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high efficacy. This is a survey chapter within which single and multi-agent systems inspired by immunology concepts are presented and analyzed. Most of the work is usually based on the adaptive immune response characteristics, such as clonal selection, idiotypic networks, and negative selection. However, the innate immune response has been neglected and there is not much work where innate metaphors are used as inspiration source to develop robotic systems. Therefore, a work that involves some interesting features of the innate and adaptive immune responses in a cognitive model for object transportation is presented at the end of this chapter.

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