Explaining online reinforcement learning decisions of self-adaptive systems
Design time uncertainty poses an important challenge when developing a self-adaptive
system. As an example, defining how the system should adapt when facing a new …
system. As an example, defining how the system should adapt when facing a new …
Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model
S Ghanadbashi, Z Safavifar, F Taebi… - Journal of Reliable …, 2024 - Springer
Ubiquitous and pervasive systems interact with each other and perform actions favoring the
emergence of a global desired behavior. To function well, these systems need to be self …
emergence of a global desired behavior. To function well, these systems need to be self …
Configuration optimization with limited functional impact
Dealing with a large configuration space is a complex task for developers, especially when
configurations must comply with both functional constraints and non-functional goals. In this …
configurations must comply with both functional constraints and non-functional goals. In this …
A novel continual reinforcement learning-based expert system for self-optimization of soft real-time systems
Z Masood, Z Jiangbin, I Ahmad, C Dongdong… - Expert Systems with …, 2024 - Elsevier
Virtual globes are soft real-time systems, which stream multi-resolution data sets and render
world-scale landscapes in real-time. Such systems require an adaptation mechanism to …
world-scale landscapes in real-time. Such systems require an adaptation mechanism to …
Task-Agnostic Safety for Reinforcement Learning
MA Rahman, S Alqahtani - Proceedings of the 16th ACM Workshop on …, 2023 - dl.acm.org
Reinforcement learning (RL) has been an attractive potential for designing autonomous
systems due to its learning-by-exploration approach. However, this learning process makes …
systems due to its learning-by-exploration approach. However, this learning process makes …
Deep reinforcement learning strategy in automated trading systems
B Itri, Y Mohamed, Q Mohammed… - … Research in Applied …, 2023 - ieeexplore.ieee.org
Every day, a considerable amount of money is traded in the form of derivatives in global
financial markets. Artificial intelligence (AI) software is now the main competitor of traditional …
financial markets. Artificial intelligence (AI) software is now the main competitor of traditional …
A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems
B Haouari, R Mzid, O Mosbahi - Neural Computing and Applications, 2023 - Springer
This paper deals with self-adaptive real-time embedded systems (RTES). A self-adaptive
system can operate in different modes. Each mode encodes a set of real-time tasks. To be …
system can operate in different modes. Each mode encodes a set of real-time tasks. To be …
Architecting Autonomous Underwater Vehicles by Adapting Software Product Lines
C Cares, D Lühr, S Mora, C Navarro, L Olivares… - … on Integrated Computer …, 2022 - Springer
Software reuse has been one of the most profitable practices in the Software Industry.
Different software engineering approaches aim to systematically include reuse as part of …
Different software engineering approaches aim to systematically include reuse as part of …
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments
S Ghanadbashi, F Golpayegani - arXiv preprint arXiv:2405.17691, 2024 - arxiv.org
Agents, whether software or hardware, perceive their environment through sensors and act
using actuators, often operating in dynamic, partially observable settings. They face …
using actuators, often operating in dynamic, partially observable settings. They face …
Towards Architecting Sustainable MLOps: A Self-Adaptation Approach
In today's dynamic technological landscape, sustainability has emerged as a pivotal
concern, especially with respect to architecting Machine Learning enabled Systems (MLS) …
concern, especially with respect to architecting Machine Learning enabled Systems (MLS) …