scholarly journals On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

2021 ◽  
Vol 379 (2207) ◽  
pp. 20200362
Author(s):  
Yingxu Wang ◽  
Fakhri Karray ◽  
Sam Kwong ◽  
Konstantinos N. Plataniotis ◽  
Henry Leung ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Collective Intelligence ◽  
Autonomous Systems ◽  
Basic Research ◽  
Machine Intelligence ◽  
Cognitive Systems ◽  
Intelligent Machines ◽  
Autonomous Machine ◽  
Cognitive Robots ◽  
Mathematical Foundations

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.

A mind model for intelligent machine innovation using future thinking principles

2019 ◽  
Vol 30 (8) ◽  
pp. 1250-1264 ◽  
Author(s):  
Anthon P. Botha
Keyword(s):  
Intelligent Systems ◽  
Thought Experiment ◽  
Management Practice ◽  
World View ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Future Thinking ◽  
Content Type ◽  
Intelligent Machines ◽  
Autonomous Machine

Purpose The purpose of this paper is to address the possible future evolution of innovation from a human-only initiative, to human–machine co-innovation, to autonomous machine innovation and to arrive at a conceptual mind model that outlines the role of innovation regimes and innovation agents. Design/methodology/approach This is a concept paper where a theoretical “thought experiment” is done, using future thinking principles and data that originate from the literature. Findings A conceptual mind model is developed to facilitate a better understanding of complexity at the edge of innovation where intelligent machines will emerge as innovators of the cyber world. It was found that innovation will gradually evolve from a human-only activity, to human–machine co-innovation, to incidences of autonomous machine innovation, based on the growth of machine intelligence and the adoption of human–machine partnership management models in future. Research limitations/implications Very little information is available in the literature on intelligent machines doing innovation. The work is based on a theoretical approach that presents new concepts to be debated, but have not been tested in engineering and technology management practice, except for a conference presentation and academic discussion. Practical implications The current world view is that future “smartness” is only possible through the creative abilities that humans have, but as machines are entering the workplace and our daily lives, not only as static robots on a manufacturing line, but as intelligent systems with the potential to replace lawyers and accountants, doctors and teachers, companions and partners, their role in innovation in complex environments needs to be explored. Social implications Human–machine interaction is often an emotional social issue of concern in terms of the replacement of human intelligence with machine intelligence. It should be asked whether humans will or should remain in control of innovation? Artificial intelligence (AI) may complement and even substitute human intelligence, but huge value is embedded in the new goods, services and innovations AI will enable, especially in manufacturing, where value embedded in the project becomes complex and dynamic. Originality/value The thinking presented in this paper is original and should lead to debate to question the way innovation systems will work in future and inspires thinking about AI and innovation.

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.

Artificial Intelligence Basic Research Directions at the U.S. Air Force

2014 ◽  
Vol 23 (06) ◽  
pp. 1460024
Author(s):  
Peter Z. Revesz
Keyword(s):  
Artificial Intelligence ◽  
Air Force ◽  
Situational Awareness ◽  
Autonomous Systems ◽  
Basic Research ◽  
Machine Intelligence ◽  
Research Directions ◽  
Research Areas ◽  
Human Computer Interfaces ◽  
The U.S

This survey gives a review of recent artificial intelligence-related research directions that are considered priority areas by the U.S. Air Force and targeted for basic research funding by Air Force Office of Scientific Research. These research areas include space situational awareness, autonomous systems, sensing and information fusion, surveillance, navigation, robust decision making, human-computer interfaces, and computational and machine intelligence. The possible contributions of artificial intelligence to these topics will be described and illustrated whenever possible by recently awarded grants.

scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

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.

Autonomously Evolving Communication Protocols

2012 ◽  
pp. 183-204
Author(s):  
Endre Sándor Varga ◽  
Bernát Wiandt ◽  
Borbála Katalin Benko ◽  
Vilmos Simon
Keyword(s):  
Natural Selection ◽  
Genetic Programming ◽  
Programming Language ◽  
Ad Hoc ◽  
Communication Protocols ◽  
Machine Intelligence ◽  
On Line ◽  
Hoc Networks ◽  
Autonomous Machine ◽  
Delay Tolerant

While traditional telecommunication still relies on rigid, highly regulated, and highly controlled communication protocols, with the emergence of new forms of networks (mobile ad hoc and delay-tolerant networks, lacking central infrastructure and strict regulations) bio-inspired communication protocols have also found their way to success. In this chapter we introduce a nontraditional way of creating and shaping communication protocols, through an autonomous machine intelligence model, built upon on-line evolutionary methods such as natural selection and genetic programming. Creating a genetic programming language and a selection mechanism for multi-hop broadcast protocols in ad hoc networks, we show that this kind of approach can outperform traditional ones under given circumstances, offering a powerful alternative in the future.

scholarly journals Abstract Intelligence

2017 ◽  
Vol 11 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yingxu Wang ◽  
Lotfi A. Zadeh ◽  
Bernard Widrow ◽  
Newton Howard ◽  
Françoise Beaufays ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mathematical Models ◽  
Computational Intelligence ◽  
Cognitive Learning ◽  
Cognitive Systems ◽  
Stanford University ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Basic Studies ◽  
Cognitive Robots

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Reciprocity between second-person neuroscience and cognitive robotics

2013 ◽  
Vol 36 (4) ◽  
pp. 418-419
Author(s):  
Peter Ford Dominey
Keyword(s):  
Dark Matter ◽  
Cognitive Robotics ◽  
Robotic Systems ◽  
Cognitive Systems ◽  
Second Person ◽  
Dynamic Interactions ◽  
Cognitive Robots

AbstractAs there is “dark matter” in the neuroscience of individuals engaged in dynamic interactions, similar dark matter is present in the domain of interaction between humans and cognitive robots. Progress in second-person neuroscience will contribute to the development of robotic cognitive systems, and such developed robotic systems will be used to test the validity of the underlying theories.

scholarly journals ENTERPRISE ENGINEERING AND INTELLECTUAL TECHNOLOGIES FOR LIFECYCLE MANAGEMENT OF INDUSTRIAL PRODUCTION

2021 ◽  
Vol 2021 (3) ◽  
pp. 50-53
Author(s):  
Nadirbek Yusupbekov ◽  
◽  
Valery Tarasov ◽  
Shukhrat Gulyamov ◽  
Fahritdin Abdurasulov ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Production Management ◽  
Life Cycles ◽  
Mathematical Methods ◽  
Enterprise Engineering ◽  
Industrial Enterprises ◽  
Modeling Methods ◽  
Interdisciplinary Field ◽  
Mathematical Foundations ◽  
Lifecycle Management

The fundamental scientific problem of the development of the mathematical foundations of engineering for industrial enterprises and the development of mathematical methods of production management, as well as the creation of intelligent systems for coordinated management of the life cycles of products and production in the network of enterprises are discussed. The issues in demand in the development of a vast interdisciplinary field of enterprise engineering and the development of modern network enterprises and intelligent production using mathematical modeling methods are discussed.

scholarly journals Understanding and Measuring Collective Intelligence Across Different Cognitive Systems: An Information-Theoretic Approach

2017 ◽  
Author(s):  
Nader Chmait
Keyword(s):  
Social Sciences ◽  
Collective Intelligence ◽  
Cognitive Systems ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Research Areas ◽  
Fundamental Biology ◽  
Human Animal ◽  
Information Theoretic Approach ◽  
Business And Management

We develop the idea of collective intelligence by analysing a range of factors hindering the effectiveness of interactive cognitive agents. We give insights into how to explore the potential of collectives across different cognitive systems (human, animal and machine) and research areas. The endeavour is to bridge the different research disciplines in which collective intelligence might occur and apply the studies of intelligence in AI to other fields, thereby cross-fertilising diverse areas of study ranging from business and management to social sciences and fundamental biology.

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