scholarly journals Sharpening the Scythe of Technological Change: Socio-Technical Challenges of Autonomous and Adaptive Cyber-Physical Systems

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 52 ◽  
Author(s):  
Daniela Cancila ◽  
Jean-Louis Gerstenmayer ◽  
Huascar Espinoza ◽  
Roberto Passerone
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Paradigm Change ◽  
Safety Engineering ◽  
Technical Aspects ◽  
Physical Systems ◽  
Safety Critical ◽  
New Knowledge ◽  
Self Adaptation

Autonomous and Adaptative Cyber-Physical Systems (ACPS) represent a new knowledge frontier of converging “nano-bio-info-cogno” technologies and applications. ACPS have the ability to integrate new `mutagenic’ technologies, i.e., technologies able to cause mutations in the society. Emerging approaches, such as artificial intelligence techniques and deep learning, enable exponential speedups for supporting increasingly higher levels of autonomy and self-adaptation. In spite of this disruptive landscape, however, deployment and broader adoption of ACPS in safety-critical scenarios remains challenging. In this paper, we address some challenges that are stretching the limits of ACPS safety engineering, including tightly related aspects such as ethics and resilience. We argue that a paradigm change is needed that includes the entire socio-technical aspects, including trustworthiness, responsibility, liability, as well as the ACPS ability to learn from past events, anticipate long-term threads and recover from unexpected behaviors.

scholarly journals Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems

2020 ◽  
Vol 2 (4) ◽  
pp. 579-602
Author(s):  
Ana Pereira ◽  
Carsten Thomas
Keyword(s):  
Machine Learning ◽  
Autonomous Driving ◽  
Cyber Physical Systems ◽  
Shop Floor ◽  
Safety Engineering ◽  
Safety Hazards ◽  
Physical Systems ◽  
Safety Critical ◽  
Holistic Approaches ◽  
Transportation Vehicle

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

Safety-Critical, Dependable, and Fault-Tolerant Cyber-Physical Systems

2018 ◽  
pp. 54-78
Author(s):  
Guru Prasad Bhandari ◽  
Ratneshwer Gupta
Keyword(s):  
Fault Tolerance ◽  
Fault Tolerant ◽  
Cyber Physical Systems ◽  
General Information ◽  
The Internet ◽  
Physical Systems ◽  
Safety Critical ◽  
Computer Based

Cyber-physical systems (CPSs) are co-engineered integrating with physical and computational components networks. Additionally, a CPS is a mechanism controlled or monitored by computer-based algorithms, tightly interacting with the internet and its users. This chapter presents the definitions relating to dependability, safety-critical and fault-tolerance of CPSs. These definitions are supplemented by other definitions like reliability, availability, safety, maintainability, integrity. Threats to dependability and security like faults, errors, failures are also discussed. Taxonomy of different faults and attacks in CPSs are also presented in this chapter. The main objective of this chapter is to give the general information about secure CPS to the learners for the further enhancement in the field of CPSs.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

Sign in / Sign up

Export Citation Format

Share Document