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Self-Governing Hybrid Societies and Deception ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-04-20 Ştefan Sarkadi
Self-governing hybrid societies are multi-agent systems where humans and machines interact by adapting to each other’s behaviour. Advancements in Artificial Intelligence (AI) have brought an increasing hybridisation of our societies, where one particular type of behaviour has become more and more prevalent, namely deception. Deceptive behaviour as the propagation of disinformation can have negative
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Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-04-20 Saim Sunel, Erkin Çilden, Faruk Polat
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
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Decision Making for Self-Adaptation Based on Partially Observable Satisfaction of Non-Functional Requirements ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-04-20 Luis Garcia, Huma Samin, Nelly Bencomo
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
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A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-04-20 Nianyu Li, Mingyue Zhang, Jialong Li, Sridhar Adepu, Eunsuk Kang, Zhi Jin
Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior
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Applying Trust for Operational States of ICT-Enabled Power Grid Services ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-04-03 Michael Brand, Anand Narayan, Sebastian Lehnhoff
Digitalization enables the automation required to operate modern cyber-physical energy systems (CPESs), leading to a shift from hierarchical to organic systems. However, digitalization increases the number of factors affecting the state of a CPES (e.g., software bugs and cyber threats). In addition to established factors like functional correctness, others like security become relevant but are yet
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Self-Adapting Machine Learning-based Systems via a Probabilistic Model Checking Framework ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-03-07 Maria Casimiro, Diogo Soares, David Garlan, Luís Rodrigues, Paolo Romano
This paper focuses on the problem of optimizing system utility of Machine-Learning (ML) based systems in the presence of ML mispredictions. This is achieved via the use of self-adaptive systems and through the execution of adaptation tactics, such as model retraining, which operate at the level of individual ML components. To address this problem, we propose a probabilistic modeling framework that
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Anunnaki: A Modular Framework for Developing Trusted Artificial Intelligence ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-03-06 Michael Austin Langford, Sol Zilberman, Betty H.C. Cheng
Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and interpretability of learning models. In addition to developing strategies to address these concerns, appropriate software architectures are needed
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Human–machine Teaming with Small Unmanned Aerial Systems in a MAPE-K Environment ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Jane Cleland-Huang, Theodore Chambers, Sebastian Zudaire, Muhammed Tawfiq Chowdhury, Ankit Agrawal, Michael Vierhauser
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
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Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Jia Li, Shiva Nejati, Mehrdad Sabetzadeh
Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that
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Self-Adaptive Testing in the Field ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Samira Silva, Patrizio Pelliccione, Antonia Bertolino
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, and so on. 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
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Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Omid Gheibi, Danny Weyns
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 article, we focus on a particularly important challenge for learning-based self-adaptive
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Predicting Nonfunctional Requirement Violations in Autonomous Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Xinwei Fang, Sinem Getir Yaman, Radu Calinescu, Julie Wilson, Colin Paterson
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
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NEPTUNE: A Comprehensive Framework for Managing Serverless Functions at the Edge ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2024-02-14 Luciano Baresi, Davide Yi Xian Hu, Giovanni Quattrocchi, Luca Terracciano
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
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EdgeMart: A Sustainable Networked OTT Economy on the Wireless Edge for Saving Multimedia IP Bandwidth ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-10-14 Ranjan Pal, Nishanth Sastry, Emeka Obiodu, Sanjana Prabhu, Konstantinos Psounis
With the advent of 5G+ services, it has become increasingly convenient for mobile users to enjoy high-quality multimedia content from CDN driven streaming and catch-up TV services (Netflix, iPlayer) in the (post-) COVID over-the-top (OTT) content rush. To relieve ISP owned fixed-line networks from CDN streamed multimedia traffic, system ideas (e.g., Wi-Stitch in [45]) have been proposed to (a) leverage
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Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous Resources ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-10-14 Gabriele Russo Russo, Valeria Cardellini, Francesco Lo Presti
Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in the face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism
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Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-10-14 Thuy Ngoc Nguyen, Duy Nhat Phan, Cleotilde Gonzalez
Developing effective multi-agent systems (MASs) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of multi-agent deep reinforcement learning (MADRL) in cooperative MASs, 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
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Self-Adaptive Testing in the Field ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-10-11 Samira Silva, Patrizio Pelliccione, Antonia Bertolino
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
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Self-aware Optimization of Adaptation Planning Strategies ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-09-20 Veronika Lesch, Marius Hadry, Christian Krupitzer, Samuel Kounev
In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. To operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to
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Improving Causal Learning Scalability and Performance using Aggregates and Interventions ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-09-22 Kanvaly Fadiga, Etienne Houzé, Ada Diaconescu, Jean-Louis Dessalles
Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows
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Foreword: ACSOS 2021 Special Issue ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-09-20 Danilo Pianini, Vana Kalogeraki
No abstract available.
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Using Randomization in Self-organized Synchronization for Wireless Networks ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-09-20 Jorge F. Schmidt, Udo Schilcher, Arke Vogell, Christian Bettstetter
The concept of pulse-coupled oscillators for self-organized synchronization has been applied to wireless systems. Putting theory into practice, however, faces certain obstacles, particularly in radio technologies that cannot implement pulses but use common messages for interactions between nodes. This raises the question of how to deal with interference between messages. We show that interference can
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Enforcing Resilience in Cyber-physical Systems via Equilibrium Verification at Runtime ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-09-20 Matteo Camilli, Raffaela Mirandola, Patrizia Scandurra
Cyber-physical systems often operate in dynamic environments where unexpected events should be managed while guaranteeing acceptable behavior. Providing comprehensive evidence of their dependability under change represents a major open challenge. In this article, we exploit the notion of equilibrium, that is, the ability of the system to maintain an acceptable behavior within its multidimensional viability
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Improving Causal Learning Scalability and Performance using Aggregates and Interventions ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-07-25 Kanvaly Fadiga, Etienne Houzé, Ada Diaconescu, Jean-Louis Dessalles
Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows
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Using Randomization in Self-Organized Synchronization for Wireless Networks ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-06-21 Jorge F. Schmidt, Udo Schilcher, Arke Vogell, Christian Bettstetter
The concept of pulse-coupled oscillators for self-organized synchronization has been applied to wireless systems. Putting theory into practice, however, faces certain obstacles, particularly in radio technologies that cannot implement pulses but use common messages for interactions between nodes. This raises the question of how to deal with interference between messages. We show that interference can
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EdgeMart: A Sustainable Networked OTT Economy on the Wireless Edge for Saving Multimedia IP Bandwidth ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-06-20 Ranjan Pal, Nishanth Sastry, Emeka Obiodu, Sanjana Prabhu, Konstantinos Psounis
With the advent of 5G+ services, it has become increasingly convenient for mobile users to enjoy high quality multimedia content from CDN driven streaming and catch-up TV services (Netflix, iPlayer) in the (post-)COVID over-the-top (OTT) content rush. To relieve ISP owned fixed-line networks from CDN streamed multimedia traffic, system ideas (e.g., Wi-Stitch in [45]) have been proposed to (a) leverage
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Modeling, Replicating, and Predicting Human Behavior: A Survey ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-05-28 Andrew Fuchs, Andrea Passarella, Marco Conti
Given the popular presupposition of human reasoning as the standard for learning and decision making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, and Machine Learning, among others, integrate concepts that are assumed components of human reasoning. These serve
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Self-Adaptation in Industry: A Survey ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-05-28 Danny Weyns, Ilias Gerostathopoulos, Nadeem Abbas, Jesper Andersson, Stefan Biffl, Premek Brada, Tomas Bures, Amleto Di Salle, Matthias Galster, Patricia Lago, Grace Lewis, Marin Litoiu, Angelika Musil, Juergen Musil, Panos Patros, Patrizio Pelliccione
Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to
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A Genetic Programming-based Framework for Semi-automated Multi-agent Systems Engineering ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-05-28 Nicola Mc Donnell, Jim Duggan, Enda Howley
With the rise of new technologies, such as Edge computing, Internet of Things, Smart Cities, and Smart Grids, there is a growing need for multi-agent systems (MAS) approaches. Designing multi-agent systems is challenging, and doing this in an automated way is even more so. To address this, we propose a new framework, Evolved Gossip Contracts (EGC). It builds on Gossip Contracts (GC), a decentralised
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GLDAP: Global Dynamic Action Persistence Adaptation for Deep Reinforcement Learning ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-05-28 Junbo Tong, Daming Shi, Yi Liu, Wenhui Fan
In the implementation of deep reinforcement learning (DRL), action persistence strategies are often adopted so agents maintain their actions for a fixed or variable number of steps. The choice of the persistent duration for agent actions usually has notable effects on the performance of reinforcement learning algorithms. Aiming at the research gap of global dynamic optimal action persistence and its
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Hierarchical Auto-Scaling Policies for Data Stream Processing on Heterogeneous Resources ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-05-16 Gabriele Russo Russo, Valeria Cardellini, Francesco Lo Presti
Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism
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On Understanding Context Modelling for Adaptive Authentication Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-03-31 Anne Bumiller, Stéphanie Challita, Benoit Combemale, Olivier Barais, Nicolas Aillery, Gael Le Lan
In many situations, it is of interest for authentication systems to adapt to context (e.g., when the user’s behavior differs from the previous behavior). Hence, representing the context with appropriate and well-designed models is crucial. We provide a comprehensive overview and analysis of research work on Context Modelling for Adaptive Authentication systems (CM4AA). To this end, we pursue three
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Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-03-27 Jad Bassil, Abdallah Makhoul, Benoît Piranda, Julien Bourgeois
Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules that are able to coordinate to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay, and improving the self-reconfiguration process that focuses on
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Model-driven Cluster Resource Management for AI Workloads in Edge Clouds ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-03-27 Qianlin Liang, Walid A. Hanafy, Ahmed Ali-Eldin, Prashant Shenoy
Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently proposed to speed up deep neural network (DNN) inference run by these applications. Resource-constrained edge servers and accelerators tend to be multiplexed across multiple IoT applications, introducing the potential for performance
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Enforcing Resilience in Cyber-physical Systems via Equilibrium Verification at Runtime ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2023-02-16 Matteo Camilli, Raffaela Mirandola, Patrizia Scandurra
Cyber-Physical Systems often operate in dynamic environments where unexpected events should be managed while guaranteeing acceptable behavior. Providing comprehensive evidence of their dependability under change represents a major open challenge. In this paper, we exploit the notion of equilibrium, that is, the ability of the system to maintain an acceptable behavior within its multidimensional viability
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Modeling and Analysis of Explanation for Secure Industrial Control Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-12-15 Sridhar Adepu, Nianyu Li, Eunsuk Kang, David Garlan
Many self-adaptive systems benefit from human involvement and oversight, where a human operator can provide expertise not available to the system and detect problems that the system is unaware of. One way of achieving this synergy is by placing the human operator on the loop—i.e., providing supervisory oversight and intervening in the case of questionable adaptation decisions. To make such interaction
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Formally Verified Scalable Look Ahead Planning For Cloud Resource Management ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-12-15 Farzin Zaker, Marin Litoiu, Mark Shtern
In this article, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows
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Self-Aware Optimization of Adaptation Planning Strategies ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-10-25 Veronika Lesch, Marius Hadry, Samuel Kounev, Christian Krupitzer
In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles
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Dynamic System Diversification for Securing Cloud-based IoT Subnetworks ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-09-07 Hussain Almohri, Layne Watson, David Evans, Stephen Billups
Remote exploitation attacks use software vulnerabilities to penetrate through a network of Internet of Things (IoT) devices. This work addresses defending against remote exploitation attacks on vulnerable IoT devices. As an attack mitigation strategy, we assume it is not possible to fix all the vulnerabilities and propose to diversify the open-source software used to manage IoT devices. Our approach
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Prosocial Norm Emergence in Multi-agent Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-09-07 Mehdi Mashayekhi, Nirav Ajmeri, George F. List, Munindar P. Singh
Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It
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A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-09-07 Danilo Pianini, Federico Pettinari, Roberto Casadei, Lukas Esterle
We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new
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Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-29 Danny Weyns, Omid Gheibi, Federico Quin, Jeroen Van Der Donckt
Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option
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A Collective Adaptive Approach to Decentralised k-Coverage in Multi-Robot Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-18 Danilo Pianini, Federico Pettinari, Roberto Casadei, Lukas Esterle
We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: rather than classically developing new
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Systematic Scalability Modeling of QoS-Aware Dynamic Service Composition ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-12 Leticia Duboc, Rami Bahsoon, Faisal Alrebeish, Carlos Mera-Gómez, Vivek Nallur, Rick Kazman, Philip Bianco, Muhammad Ali Babar, Rajkumar Buyya
In Dynamic Service Composition(DSC), an application can be dynamically composed using web services to achieve its functional and Quality of Services (QoS) goals. DSC is a relatively mature area of research that crosscuts autonomous and services computing. Complex autonomous and self-adaptive computing paradigms (e.g. multi-tenant cloud services, mobile/smart services, services discovery and composition
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Dynamic System Diversification for Securing Cloud-based IoT Subnetworks ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-11 Hussain M. J. Almohri, Layne T. Watson, David Evans, Stephen Billups
Remote exploitation attacks use software vulnerabilities to penetrate through a network of Internet of Things (IoT) devices. This work addresses defending against remote exploitation attacks on vulnerable IoT devices. As an attack mitigation strategy, we assume it is not possible to fix all the vulnerabilities and propose to diversify the open-source software used to manage IoT devices. Our approach
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Assured Mission Adaptation of UAVs ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-06 Sebastián A. Zudaire, Leandro Nahabedian, Sebastián Uchitel
The design of systems that can change their behaviour to account for scenarios that were not foreseen at design time remains an open challenge. In this article, we propose an approach for adaptation of mobile robot missions that is not constrained to a predefined set of mission evolutions. We implement an adaptive software architecture and show how controller synthesis can be used both to guarantee
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Risk-aware Collection Strategies for Multirobot Foraging in Hazardous Environments ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-06 Kai Di, Yifeng Zhou, Jiuchuan Jiang, Fuhan Yan, Shaofu Yang, Yichuan Jiang
Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous
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HAMLET: A Hierarchical Agent-based Machine Learning Platform ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-06 Ahmad Esmaeili, John C. Gallagher, John A. Springer, Eric T. Matson
Hierarchical Multi-agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this article, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research
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Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-07-04 Danny Weyns, Omid Gheibi, Federico Quin, Jeroen Van Der Donckt
Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option
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Prosocial Norm Emergence in Multiagent Systems ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-06-06 Mehdi Mashyekhi, Nirav Ajmeri, George F. List, Munindar P. Singh
Multiagent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multiagent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multiagent system can be reflected in the social norms among its members. It is
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HAMLET: A Hierarchical Agent-based Machine Learning Platform ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-04-24 Ahmad Esmaeili, John C. Gallagher, John A. Springer, Eric T. Matson
Hierarchical Multi-Agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this paper, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research
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Dynamic Evaluation of Microservice Granularity Adaptation ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-03-04 Sara Hassan, Rami Bahsoon, Leandro Minku, Nour Ali
Microservices have gained acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. Among the critical microservice architecture design decisions is when to adapt the granularity of a microservice architecture by merging/decomposing microservices. No existing work investigates the following question: How can we reason about the trade-off
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Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-03-04 Lukas Esterle, David W. King
Large pervasive systems, deployed in dynamic environments, require flexible control mechanisms to meet the demands of chaotic state changes while accomplishing system goals. As centralized control approaches may falter in environments where centralized communication and knowledge may be impossible to implement, researchers have proposed decentralized control methods that leverage agent-driven, self-organizing
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An Autonomous System for Efficient Control of PTZ Cameras ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-03-04 Sina G. Davani, Musab S. Al-Hadrusi, Nabil J. Sarhan
This article addresses the research problem of how to autonomously control Pan/Tilt/Zoom (PTZ) cameras in a manner that seeks to optimize the face recognition accuracy or the overall threat detection and proposes an overall system. The article presents two alternative schemes for camera scheduling: Grid-Based Grouping (GBG) and Elevator-Based Planning (EBP). The camera control works with realistic
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Computing Contingent Plan Graphs using Online Planning ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-01-22 Shlomi Maliah, Radimir Komarnitski, Guy Shani
In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. However, the translation may become very large, encumbering the task
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Gist Trace-based Learning: Efficient Convention Emergence from Multilateral Interactions ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-01-22 Shuyue Hu, Chin-Wing Leung, Ho-Fung Leung, Jiamou Liu
The concept of conventions has attracted much attention in the multi-agent system research. In this article, we study the emergence of conventions from repeated n-player coordination games. Distributed agents learn their policies independently and are capable of observing their neighbours in a network topology. We distinguish two types of information representation about the observations: gist trace
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A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2022-01-22 Muhammad Usman Arif, Sajjad Haider
In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks
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Introduction to the Special Issue with Selected Papers of The International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) 2020 ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2021-12-20 Sven Tomforde, Timothy Wood, Jan-Philipp Steghöfer
No abstract available.
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Uncertainty in Self-adaptive Systems: A Research Community Perspective ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2021-12-20 Sara M. Hezavehi, Danny Weyns, Paris Avgeriou, Radu Calinescu, Raffaela Mirandola, Diego Perez-Palacin
One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and
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Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2021-12-20 Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis, Jim Tørresen
Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating
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REACT-ION: A Model-based Runtime Environment for Situation-aware Adaptations ACM Trans. Auton. Adapt. Syst. (IF 2.7) Pub Date : 2021-12-20 Martin Pfannemüller, Martin Breitbach, Markus Weckesser, Christian Becker, Bradley Schmerl, Andy Schürr, Christian Krupitzer
Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication