Evolve the Model Universe of a System Universe

• URL: http://arxiv.org/abs/2309.13342v1
• Date: Sat, 23 Sep 2023 11:30:26 GMT
• Title: Evolve the Model Universe of a System Universe
• Authors: Tao Yue and Shaukat Ali
• Abstract summary: We present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most updated states.
• Score: 7.435569269857048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertain, unpredictable, real time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash the potential of such systems and facilitate their wider adoption, ensuring the trustworthiness of their decision making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most updated states. Such representations are often in the form of prior knowledge based and machine learning models, together called model universe. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.

Related papers

• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
  This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm. It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
  arXiv  Detail & Related papers  (2024-03-04T06:25:26Z)
• Security Challenges in Autonomous Systems Design [1.864621482724548]
  With the independence from human control, cybersecurity of such systems becomes even more critical. With the independence from human control, cybersecurity of such systems becomes even more critical. This paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions.
  arXiv  Detail & Related papers  (2023-11-05T09:17:39Z)
• Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging [0.0]
  In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.
  arXiv  Detail & Related papers  (2023-06-22T12:44:49Z)
• Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges [0.76146285961466]
  How to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge. latent vulnerabilities and privacy issues exposed to external users and attackers will be largely neglected and hard to be identified. We consider that security for machine learning-based software systems may arise from inherent system defects or external adversarial attacks.
  arXiv  Detail & Related papers  (2022-01-12T23:20:25Z)
• Systems Challenges for Trustworthy Embodied Systems [0.0]
  A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. It is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in
  arXiv  Detail & Related papers  (2022-01-10T15:52:17Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Machine Learning in Finance-Emerging Trends and Challenges [1.2891210250935146]
  The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists. With the exponential growth in processing speed, organizations have found it possible to harness a humongous volume of data in realizing solutions that have far-reaching business values. This introductory chapter highlights some of the challenges and barriers that organizations in the financial services sector at the present encounter in adopting machine learning and artificial intelligence-based models and applications in their day-to-day operations.
  arXiv  Detail & Related papers  (2021-10-08T09:14:06Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.