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- Camara Moreno, Javier, et al.
(författare)
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Towards Bridging the Gap between Control and Self-Adaptive System Properties
- 2020
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Ingår i: SEAMS '20. - New York, NY, USA : ACM. - 9781450379625 ; , s. 78-84
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Konferensbidrag (refereegranskat)abstract
- Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system’s run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.
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| 2. |
- Mallozzi, Piergiuseppe, 1990, et al.
(författare)
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A runtime monitoring framework to enforce invariants on reinforcement learning agents exploring complex environments
- 2019
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Ingår i: RoSE 2019, IEEE/ACM 2nd International Workshop on Robotics Software Engineering, p.5-12. - : IEEE. - 9781728122496
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Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE. Without prior knowledge of the environment, a software agent can learn to achieve a goal using machine learning. Model-free Reinforcement Learning (RL) can be used to make the agent explore the environment and learn to achieve its goal by trial and error. Discovering effective policies to achieve the goal in a complex environment is a major challenge for RL. Furthermore, in safety-critical applications, such as robotics, an unsafe action may cause catastrophic consequences in the agent or in the environment. In this paper, we present an approach that uses runtime monitoring to prevent the reinforcement learning agent to perform 'wrong' actions and to exploit prior knowledge to smartly explore the environment. Each monitor is de?ned by a property that we want to enforce to the agent and a context. The monitors are orchestrated by a meta-monitor that activates and deactivates them dynamically according to the context in which the agent is learning. We have evaluated our approach by training the agent in randomly generated learning environments. Our results show that our approach blocks the agent from performing dangerous and safety-critical actions in all the generated environments. Besides, our approach helps the agent to achieve its goal faster by providing feedback and shaping its reward during learning.
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| 3. |
- Weyns, Danny, et al.
(författare)
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Towards a Research Agenda for Understanding and Managing Uncertainty in Self-Adaptive Systems
- 2023
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Ingår i: Software Engineering Notes. - : ACM Publications. - 0163-5948 .- 1943-5843. ; 48:4, s. 20-36
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Tidskriftsartikel (refereegranskat)abstract
- Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years.
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| 4. |
- Weyns, Danny, et al.
(författare)
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Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
- 2021
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Ingår i: Proceedings of the 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). - : IEEE. - 9781665402897 - 9781665402903 ; , s. 217-223
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Konferensbidrag (refereegranskat)abstract
- Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.
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