Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

D'Angelo, Mirko; Ghahremani, Sona; Gerasimou, Simos; Grohmann, Johannes; Nunes, Ingrid; Tomforde, Sven; Pournaras, Evangelos

doi:10.1109/acsos-c51401.2020.00042

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…This means that approaches based on massive training data or continuous feedback/supervision by users are not feasible. In turn, the system has to figure out what to do in which situation: the classic reinforcement learning (RL) paradigm combined with further mechanisms from the domain of machine learning such as anomaly detection, transfer learning, or collaborative learning (D'Angelo et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

Reichhuber¹,

Tomforde²

2022

Preprint

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Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the learning problem and make isolated use of techniques from different field of machine learning such as reinforcement learning, active learning, anomaly detection or transfer learning, for instance. In this context, the fundamental reinforcement learning approaches come with several drawbacks that hinder their application to real-world systems: trial-and-error, purely reactive behaviour or isolated problem handling. The idea of this article is to present a concept for alleviating these drawbacks by setting up a research agenda towards what we call "active reinforcement learning" in intelligent systems.

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Section: Introductionmentioning

confidence: 99%

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

Reichhuber¹,

Tomforde²

2022

Preprint

Self Cite

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A Probabilistic Model Checking Approach to Self-adapting Machine Learning Systems

Casimiro

Garlan

Cámara

et al. 2022

Lecture Notes in Computer Science

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Sarde

Grohmann

Eismann

Bauer

et al. 2020

ACM Trans. Auton. Adapt. Syst.

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Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE , a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.

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Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

Cited by 3 publications

References 16 publications

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

A Probabilistic Model Checking Approach to Self-adapting Machine Learning Systems

Sarde

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