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"autonomous systems"
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On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems
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'.
Journal Article
The winding path towards symbiotic autonomous systems
Over the next 10 years, we are likely to see the convergence of two independent evolutionary paths: one leading to an augmentation of machine capabilities; the other with the augmentation of human capabilities. This convergence will not happen at a specific point in time; instead, it will be the result of progressive overlapping, to the point that it might be difficult to identify a defining moment. The following decade will likely be quite different from the present one. 5G will probably be remembered as a transitional system, artificial intelligence (AI) as a misplaced objective. We are looking forward to a communications fabric created by autonomous systems that will exist both in the physical world as well as in cyberspace, determining a continuum that gives rise to digital reality and where intelligence is an emerging property of the ambient. Hence, the dichotomy between AI and natural intelligence will no longer exist and AI will be considered as a tool for human augmentation and as the glue connecting minds and machines. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
Journal Article
The developmental organization of robot behavior
\"This book explores the question of how robots might \"learn\" how to behave in novel environments, without having each possible action pre-programmed\"-- Provided by publisher.
BOOK
Autonomous Weapons Systems and International Norms
In Autonomous Weapons Systems and International Norms Ingvild Bode and Hendrik Huelss present an innovative analysis of how testing, developing, and using weapons systems with autonomous features shapes ethical and legal norms, arguing that they have already established standards for what counts as meaningful human control.
eBook
DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour
The deep neural networks (DNNs)-based autonomous driving systems (ADSs) are expected to reduce road accidents and improve safety in the transportation domain as it removes the factor of human error from driving tasks. The DNN-based ADS sometimes may exhibit erroneous or unexpected behaviours due to unexpected driving conditions which may cause accidents. Therefore, safety assurance is vital to the ADS. However, DNN-based ADS is a highly complex system that puts forward a strong demand for robustness, more specifically, the ability to predict unexpected driving conditions to prevent potential inconsistent behaviour. It is not possible to generalize the DNN model’s performance for all driving conditions. Therefore, the driving conditions that were not considered during the training of the ADS may lead to unpredictable consequences for the safety of autonomous vehicles. This study proposes an autoencoder and time series analysis–based anomaly detection system to prevent the safety-critical inconsistent behaviour of autonomous vehicles at runtime. Our approach called
DeepGuard
consists of two components. The first component- the inconsistent behaviour predictor, is based on an autoencoder and time series analysis to reconstruct the driving scenarios. Based on reconstruction error (
e
) and threshold (
θ
), it determines the normal and unexpected driving scenarios and predicts potential inconsistent behaviour. The second component provides on-the-fly safety guards, that is, it automatically activates healing strategies to prevent inconsistencies in the behaviour. We evaluated the performance of
DeepGuard
in predicting the injected anomalous driving scenarios using already available open-sourced DNN-based ADSs in the Udacity simulator. Our simulation results show that the best variant of
DeepGuard
can predict up to 93% on the
CHAUFFEUR
ADS, 83% on
DAVE-2
ADS, and 80% of inconsistent behaviour on the
EPOCH
ADS model, outperforming
SELFORACLE
and
DeepRoad
. Overall,
DeepGuard
can prevent up to 89% of all predicted inconsistent behaviours of ADS by executing predefined safety guards.
Journal Article
Ethical framework for designing autonomous intelligent systems
To gain the potential benefit of autonomous intelligent systems, their design and development need to be aligned with fundamental values and ethical principles. We need new design approaches, methodologies and processes to deploy ethical thought and action in the contexts of autonomous intelligent systems. To open this discussion, this article presents a review of ethical principles in the context of artificial intelligence design, and introduces an ethical framework for designing autonomous intelligent systems. The framework is based on an iterative, multidisciplinary perspective yet a systematic discussion during an Autonomous Intelligent Systems (AIS) design process, and on relevant ethical principles for the concept design of autonomous systems. We propose using scenarios as a tool to capture the essential user's or stakeholder's specific qualitative information, which is needed for a systematic analysis of ethical issues in the specific design case.
Journal Article