ACM Transactions on Autonomous and Adaptive Systems最新文献_第2页

Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements 基于部分可观测的非功能性需求满足情况的自适应决策

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2024-02-09 DOI: 10.1145/3643889

Luis Garcia, Huma Samin, Nelly Bencomo

{"title":"Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements","authors":"Luis Garcia, Huma Samin, Nelly Bencomo","doi":"10.1145/3643889","DOIUrl":"https://doi.org/10.1145/3643889","url":null,"abstract":"Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are assumed to have known, deterministic effects over the system. However, in practice, the system’s state may not be fully observable, and the adaptation actions may produce unexpected effects due to uncertain factors. This paper presents a novel probabilistic approach to quantify the uncertainty associated with the effects of adaptation actions on the state of a SAS. Supported by Bayesian inference and POMDPs (Partially-Observable Markov Decision Processes), these effects are translated into the satisfaction levels of the non-functional requirements (NFRs) to, therefore, drive the decision-making. The approach has been applied to two substantial case studies from the networking and Internet of Things (IoT) domains, using two different POMDP solvers. The results show that the approach delivers statistically significant improvements in supporting decision-making for SAS.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"75 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters 通过调整更少的超参数，更快地识别基于 MIL 的强化学习子目标

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2024-02-05 DOI: 10.1145/3643852

Saim Sunel, Erkin Çilden, Faruk Polat

{"title":"Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters","authors":"Saim Sunel, Erkin Çilden, Faruk Polat","doi":"10.1145/3643852","DOIUrl":"https://doi.org/10.1145/3643852","url":null,"abstract":"Various methods have been proposed in the literature for identifying subgoals in discrete reinforcement learning (RL) tasks. Once subgoals are discovered, task decomposition methods can be employed to improve the learning performance of agents. In this study, we classify prominent subgoal identification methods for discrete RL tasks in the literature into the following three categories: graph-based, statistics-based, and multi-instance learning (MIL)-based. As contributions, firstly, we introduce a new MIL-based subgoal identification algorithm called EMDD-RL and experimentally compare it with a previous MIL-based method. The previous approach adapts MIL’s Diverse Density (DD) algorithm, whereas our method considers Expected-Maximization Diverse Density (EMDD). The advantage of EMDD over DD is that it can yield more accurate results with less computation demand thanks to the expectation-maximization algorithm. EMDD-RL modifies some of the algorithmic steps of EMDD to identify subgoals in discrete RL problems. Secondly, we evaluate the methods in several RL tasks for the hyperparameter tuning overhead they incur. Thirdly, we propose a new RL problem called key-room and compare the methods for their subgoal identification performances in this new task. Experiment results show that MIL-based subgoal identification methods could be preferred to the algorithms of the other two categories in practice.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"91 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Governing Hybrid Societies and Deception 自治的混合社会与欺骗

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2024-01-09 DOI: 10.1145/3638549

Ștefan Sarkadi

引用次数: 0

Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation 利用终身自适应技术处理基于学习的自适应系统中的适应空间漂移问题

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-12-13 DOI: 10.1145/3636428

Omid Gheibi, Danny Weyns

{"title":"Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation","authors":"Omid Gheibi, Danny Weyns","doi":"10.1145/3636428","DOIUrl":"https://doi.org/10.1145/3636428","url":null,"abstract":"Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"30 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138581549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NEPTUNE: a Comprehensive Framework for Managing Serverless Functions at the Edge 海王星:管理边缘无服务器功能的综合框架

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-12-04 DOI: 10.1145/3634750

Luciano Baresi, Davide Yi Xian Hu, Giovanni Quattrocchi, Luca Terracciano

{"title":"NEPTUNE: a Comprehensive Framework for Managing Serverless Functions at the Edge","authors":"Luciano Baresi, Davide Yi Xian Hu, Giovanni Quattrocchi, Luca Terracciano","doi":"10.1145/3634750","DOIUrl":"https://doi.org/10.1145/3634750","url":null,"abstract":"Applications that are constrained by low-latency requirements can hardly be executed on cloud infrastructures, given the high network delay required to reach remote servers. Multi-access Edge Computing (MEC) is the reference architecture for executing applications on nodes that are located close to users (i.e., at the edge of the network). This way, the network overhead is reduced but new challenges emerge. The resources available on edge nodes are limited, workloads fluctuate since users can rapidly change location, and complex tasks are becoming widespread (e.g., machine learning inference). To address these issues, this article presents NEPTUNE, a serverless-based framework that automates the management of large-scale MEC infrastructures. In particular, NEPTUNE provides i) the placement of serverless functions on MEC nodes according to users’ location, ii) the resolution of resource contention scenarios by avoiding that single nodes be saturated, and iii) the dynamic allocation of CPUs and GPUs to meet foreseen execution times. To assess NEPTUNE, we built a prototype based on K3S, an edge-dedicated version of Kubernetes, and executed a comprehensive set of experiments. Results show that NEPTUNE obtains a significant reduction in terms of response time, network overhead, and resource consumption compared to five state-of-the-art solutions.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":" 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138493294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Nonfunctional Requirement Violations in Autonomous Systems 预测自治系统中的非功能需求违反

4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-11-14 DOI: 10.1145/3632405

Xinwei Fang, Sinem Getir Yaman, Radu Calinescu, Julie Wilson, Colin Paterson

{"title":"Predicting Nonfunctional Requirement Violations in Autonomous Systems","authors":"Xinwei Fang, Sinem Getir Yaman, Radu Calinescu, Julie Wilson, Colin Paterson","doi":"10.1145/3632405","DOIUrl":"https://doi.org/10.1145/3632405","url":null,"abstract":"Autonomous systems are often used in applications where environmental and internal changes may lead to requirement violations. Adapting to these changes proactively, i.e., before the violations occur, is preferable to recovering from the failures that may be caused by such violations. However, proactive adaptation needs methods for predicting requirement violations timely, accurately and with acceptable overheads. To address this need, we present a method that allows autonomous systems to predict violations of performance, dependability and other nonfunctional requirements, and therefore take preventative measures to avoid or otherwise mitigate them. Our method for pre dicting these autonomou s sys t em disrupti o ns (PRESTO) comprises a design time stage and a run-time stage. At design-time, we use parametric model checking to obtain algebraic expressions that formalise the relationships between the nonfunctional properties of the requirements of interest (e.g., reliability, response time and energy use) and the parameters of the system and its environment. At run-time, we predict future changes in these parameters by applying piece-wise linear regression to online data obtained through monitoring, and we use the algebraic expressions to predict the impact of these changes on the system requirements. We demonstrate the application of PRESTO through simulation in case studies from two different domains.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"34 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Adaptive Testing in the Field 现场自适应测试

4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-10-11 DOI: 10.1145/3627163

Samira Silva, Patrizio Pelliccione, Antonia Bertolino

{"title":"Self-Adaptive Testing in the Field","authors":"Samira Silva, Patrizio Pelliccione, Antonia Bertolino","doi":"10.1145/3627163","DOIUrl":"https://doi.org/10.1145/3627163","url":null,"abstract":"We are increasingly surrounded by systems connecting us with the digital world and facilitating our life by supporting our work, leisure, activities at home, health, etc. These systems are pressed by two forces. On the one side, they operate in environments that are increasingly challenging due to uncertainty and uncontrollability. On the other side, they need to evolve, often in a continuous fashion, to meet changing needs, to offer new functionalities, or also to fix emerging failures. To make the picture even more complex, these systems rarely work in isolation and often need to collaborate with other systems, as well as humans. All such facets call for moving their validation during operation, as offered by approaches called testing in the field. In this paper, we observe that even the field-based testing approaches should change over time to follow and adapt to the changes and evolution of collaborating systems or environments or users’ behaviors. We provide a taxonomy of this new category of testing that we call self-adaptive testing in the field (SATF), together with a reference architecture for SATF approaches. To achieve this objective, we surveyed the literature and collected feedback and contributions from experts in the domain via a questionnaire and interviews.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foreword: ACSOS 2021 Special Issue 前言:ACSOS 2021特刊

4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-09-20 DOI: 10.1145/3612929

Danilo Pianini, Vana Kalogeraki

引用次数: 0

Human-Machine Teaming with small Unmanned Aerial Systems in a MAPE-K Environment MAPE-K环境下小型无人机系统的人机协作

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-09-04 DOI: 10.1145/3618001

Jane Cleland-Huang, Theodore Chambers, Sebastián Zudaire, Muhammed Tawfiq Chowdhury, Ankit Agrawal, Michael Vierhauser

{"title":"Human-Machine Teaming with small Unmanned Aerial Systems in a MAPE-K Environment","authors":"Jane Cleland-Huang, Theodore Chambers, Sebastián Zudaire, Muhammed Tawfiq Chowdhury, Ankit Agrawal, Michael Vierhauser","doi":"10.1145/3618001","DOIUrl":"https://doi.org/10.1145/3618001","url":null,"abstract":"The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this paper, we, therefore, present the MAPE-KHMT framework which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop in order to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-KHMT in both a simulated and physical environment, and discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43742599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models 基于认知和机器模型的协作多智能体系统学习

IF 2.7 4区计算机科学

ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2023-08-18 DOI: 10.1145/3617835

T. Nguyen, D. Phan, Cleotilde González

{"title":"Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models","authors":"T. Nguyen, D. Phan, Cleotilde González","doi":"10.1145/3617835","DOIUrl":"https://doi.org/10.1145/3617835","url":null,"abstract":"Developing effective Multi-Agent Systems (MAS) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of Multi-Agent Deep Reinforcement Learning (MADRL) in cooperative MAS, one of the major challenges that remain is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs) wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to nonstationary environments that require coordination among people. In this paper, motivated by the demonstrated ability of cognitive models based on Instance-Based Learning Theory (IBLT) to capture human decisions in many dynamic decision making tasks, we propose three variants of Multi-Agent IBL models (MAIBL). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MAS in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45483870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0