scholarly journals AUTONOMOUS SYSTEMS: COGNITIVE CALCULATIONS ON THE PRINCIPLES OF THE BOUNDARY GENERALIZATIONS PARADIGM

2021 ◽  
pp. 263-267
Author(s):  
Yurii Prokopchuk
Keyword(s):  
Intelligent Systems ◽  
Data Science ◽  
Autonomous Systems ◽  
Cognitive Networks ◽  
Sense Making ◽  
Space Robotics ◽  
Software Support ◽  
Physical Systems ◽  
Operational Systems ◽  
Living Being

Research in the field of Autonomous Systems focuses on the development of machines and robots that are able to perceive their environment autonomously and to interact with it like a living being. This field of research includes such areas as Autonomous Intelligent Systems, Cognitive Technical Systems, Autonomous Perception and Decision Making, Cognitive/Urgent Computation, Cyber-Physical Systems, Artificial Intelligence (AI), AI Assistants, Sense-Making Platform, Cognitive Operational Systems, Cognitive Networks/Internet, Autonomous Space Robotics, Machine Learning, Big Data Calculus, Data Science Machine Eliminates Human Intuition, and simulation. The report examines the mathematical and software support of autonomous systems. The necessity of deep intellectualization of autonomous systems for space purposes is substantiated.

THE CONCEPT OF AUTONOMOUS SYSTEMS IN INDUSTRY 4.0

2019 ◽  
Vol 12 (1) ◽  
pp. 77-87
Author(s):  
György Kovács ◽  
Rabab Benotsmane ◽  
László Dudás
Keyword(s):  
Industry 4.0 ◽  
Intelligent Systems ◽  
Autonomous Systems ◽  
Essential Element ◽  
Industrial Sector ◽  
Life Cycles ◽  
Multi Agent Systems ◽  
New Paradigm ◽  
Intelligent Machines ◽  
Digital Network

Recent tendencies – such as the life-cycles of products are shorter while consumers require more complex and more unique final products – poses many challenges to the production. The industrial sector is going through a paradigm shift. The traditional centrally controlled production processes will be replaced by decentralized control, which is built on the self-regulating ability of intelligent machines, products and workpieces that communicate with each other continuously. This new paradigm known as Industry 4.0. This conception is the introduction of digital network-linked intelligent systems, in which machines and products will communicate to one another in order to establish smart factories in which self-regulating production will be established. In this article, at first the essence, main goals and basic elements of Industry 4.0 conception is described. After it the autonomous systems are introduced which are based on multi agent systems. These systems include the collaborating robots via artificial intelligence which is an essential element of Industry 4.0.

Introduction to the Special Section on Data Science for Cyber-Physical Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 1-7
Author(s):  
Francesco Piccialli ◽  
Nik Bessis ◽  
Gwanggil Jeon ◽  
Calton Pu
Keyword(s):  
Data Science ◽  
Special Section ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1080
Author(s):  
Min Zhao ◽  
Zhenbo Ning ◽  
Baicun Wang ◽  
Chen Peng ◽  
Xingyu Li ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Intelligent Manufacturing ◽  
Type I ◽  
Type Ii ◽  
Multiple Perspectives ◽  
Ternary Interaction ◽  
Physical Systems ◽  
Type Iii ◽  
Dynamic Level ◽  
Systematic Scheme

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.

scholarly journals Responsible Autonomy

2017 ◽  
Author(s):  
Virginia Dignum
Keyword(s):  
Design Process ◽  
Ethical Behavior ◽  
Cultural Context ◽  
Intelligent Systems ◽  
Autonomous Systems ◽  
Research Direction ◽  
Ethical Implications ◽  
Technological Developments

As intelligent systems are increasingly making decisions that directly affect society, perhaps the most important upcoming research direction in AI is to rethink the ethical implications of their actions. Means are needed to integrate moral, societal and legal values with technological developments in AI, both during the design process as well as part of the deliberation algorithms employed by these systems. In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems. Given that ethics are dependent on the socio-cultural context and are often only implicit in deliberation processes, methodologies are needed to elicit the values held by designers and stakeholders, and to make these explicit leading to better understanding and trust on artificial autonomous systems.

Data Science and Innovations for Intelligent Systems

2021 ◽  
Author(s):  
Kavita Taneja ◽  
Harmunish Taneja ◽  
Kuldeep Kumar ◽  
Arvind Selwal ◽  
Eng Lieh Ouh
Keyword(s):  
Intelligent Systems ◽  
Data Science

The Semiotics of Cybernetic Percept-Action Systems

2011 ◽  
Vol 1 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Peter Cariani
Keyword(s):  
Performance Measures ◽  
Physical Systems ◽  
Action Systems ◽  
Sign Systems ◽  
Operational Systems ◽  
Functional Sign ◽  
Physical Construction ◽  
Semantic Closure ◽  
Operational Structure

In this paper, a semiotic framework for natural and artificial adaptive percept-action systems is presented. The functional organizations and operational structures of percept-action systems with different degrees of adaptivity and self-construction are considered in terms of syntactic, semantic, and pragmatic relations. Operational systems-theoretic criteria for distinguishing semiotic, sign-systems from nonsemiotic physical systems are proposed. A system is semiotic if a set of functional sign-states can be identified, such that the system’s behavior can be effectively described in terms of operations on sign-types. Semiotic relations involved in the operational structure of the observer are outlined and illustrated using the Hertzian commutation diagram. Percept-action systems are observers endowed with effectors that permit them to act on their surrounds. Percept-action systems consist of sensors, effectors, and a coordinative part that determines which actions will be taken. Cybernetic systems adaptively steer behavior by altering percept-action mappings contingent on evaluated performance measures via embedded goals. Self-constructing cybernetic systems use signs to direct the physical construction of all parts of the system to create new syntactic, semantic, and pragmatic relations. When a system gains the ability to construct its material hardware and choose its semiotic relations, it achieves a degree of epistemic autonomy, semantic closure, and pragmatic self-direction.

scholarly journals Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 59
Author(s):  
Fabrizio De Vita ◽  
Dario Bruneo
Keyword(s):  
Intelligent Systems ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Training Data ◽  
Cloud Platform ◽  
Model Accuracy ◽  
Training Time ◽  
Physical Systems ◽  
The Internet Of Things ◽  
And Training

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.

scholarly journals Common Data Fusion Framework: An open-source Common Data Fusion Framework for space robotics

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091176
Author(s):  
Raul Dominguez ◽  
Mark Post ◽  
Alexander Fabisch ◽  
Romain Michalec ◽  
Vincent Bissonnette ◽  
...  
Keyword(s):  
Data Fusion ◽  
Autonomous Systems ◽  
Vital Role ◽  
Sensor Data ◽  
Space Robotics ◽  
Multisensor Data Fusion ◽  
Framework Approach ◽  
The Common ◽  
Fusion Framework ◽  
Multisensor Data

Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Framework and explain how it is used. Sensor data are registered and fused within the Common Data Fusion Framework to produce comprehensive 3D environment representations and pose estimations. The proposed software components to model this process in a reusable manner are presented through a complete overview of the framework, then the provided data fusion algorithms are listed, and through the case of 3D reconstruction from 2D images, the Common Data Fusion Framework approach is exemplified. The Common Data Fusion Framework has been deployed and tested in various scenarios that include robots performing operations of planetary rover exploration and tracking of orbiting satellites.

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