Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

• URL: http://arxiv.org/abs/2103.10847v1
• Date: Fri, 19 Mar 2021 15:00:08 GMT
• Title: Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
• Authors: Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei
• Abstract summary: Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing loop, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. We are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with machine learning 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.
• Score: 16.998805882711864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments [50.310636905746975]
  Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process. Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. We propose self-healing machine learning (SHML) to overcome these limitations.
  arXiv  Detail & Related papers  (2024-10-31T20:05:51Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Compete and Compose: Learning Independent Mechanisms for Modular World Models [57.94106862271727]
  We present COMET, a modular world model which leverages reusable, independent mechanisms across different environments. COMET is trained on multiple environments with varying dynamics via a two-step process: competition and composition. We show that COMET is able to adapt to new environments with varying numbers of objects with improved sample efficiency compared to more conventional finetuning approaches.
  arXiv  Detail & Related papers  (2024-04-23T15:03:37Z)
• Reimagining Self-Adaptation in the Age of Large Language Models [0.9999629695552195]
  This paper presents a vision for using Generative AI (GenAI) to enhance the effectiveness and efficiency of architectural adaptation. Drawing parallels with human operators, we propose that Large Language Models (LLMs) can autonomously generate context-sensitive adaptation strategies. Our findings suggest that GenAI has significant potential to improve software systems' dynamic adaptability and resilience.
  arXiv  Detail & Related papers  (2024-04-15T15:30:12Z)
• Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems [0.0]
  We propose the integration of large language models (LLMs) into multiagent systems. We anchor our methodology on the MAPE-K model, which is renowned for its robust support in monitoring, analyzing, planning, and executing system adaptations.
  arXiv  Detail & Related papers  (2023-07-12T14:26:46Z)
• Reducing Large Adaptation Spaces in Self-Adaptive Systems Using Machine Learning [10.444983001376874]
  We present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals.
  arXiv  Detail & Related papers  (2023-06-02T09:49:33Z)
• Human Machine Co-adaption Interface via Cooperation Markov Decision Process System [8.68491060014975]
  This paper introduces the co-adaption techniques via model-based reinforcement learning. In this study, we treat the full process of robot-assisted rehabilitation as a co-adaptive or mutual learning process.
  arXiv  Detail & Related papers  (2023-05-03T12:00:53Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems [17.93069260609691]
  We use computational learning theory to determine a theoretical bound on the impact of the machine learning method on the predictions made by the verifier. To conclude, we look at opportunities for future research in this area.
  arXiv  Detail & Related papers  (2021-03-18T11:59:50Z)
• Meta-Learning with Neural Tangent Kernels [58.06951624702086]
  We propose the first meta-learning paradigm in the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's Neural Tangent Kernel (NTK) Within this paradigm, we introduce two meta-learning algorithms, which no longer need a sub-optimal iterative inner-loop adaptation as in the MAML framework. We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.
  arXiv  Detail & Related papers  (2021-02-07T20:53:23Z)
• Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision [74.9260745577362]
  This paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC) principles. We construct three propagative modules to effectively solve the optimization models with flexible combinations. Experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.
  arXiv  Detail & Related papers  (2020-12-10T03:24:53Z)

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.