ACM Transactions on

Autonomous and Adaptive Systems (TAAS)

Latest Articles

Knowledge Management for Self-Organised Resource Allocation

Many instances of socio-technical systems in the digital society and digital economy require some form of self-governance. Examples include community... (more)

Designing Robot Teams for Distributed Construction, Repair, and Maintenance

Designing teams of autonomous robots that can create target structures or repair damage to those structures on either a one-off or ongoing basis is an... (more)

Runtime Monitoring and Resolution of Probabilistic Obstacles to System Goals

Software systems are deployed in environments that keep changing over time. They should therefore adapt to changing conditions to meet their... (more)

Mutual Influence-aware Runtime Learning of Self-adaptation Behavior

Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation... (more)


New EiC

ACM Transactions on Autonomous and Adaptive Systems Names Bashar Nuseibeh as EiC

ACM Transactions on Autonomous and Adaptive Systems (TAAS) has named Bashar Nuseibeh as Editor-in-Chief, for the term October 1, 2017 to September 30, 2020. Bashar is a Professor of Computing at The Open University, UK,  and a Professor of Software Engineering at Lero - The Irish Software Research Centre. He is also Visiting Professor at University College London and at the National Institute of Informatics in Japan.

Argumentation-based Reasoning about Plans, Maintenance Goals and Norms

In a normative environment an agent?s actions are not only directed by its goals, but also by the norms activated by its actions and those of other actors. The potential for conflict between agent goals and norms makes decision-making challenging, in that it requires looking-ahead to consider the longer term consequences of which goal to satisfy or which norm to comply with in face of conflict. We therefore seek to determine the actions an agent should select at each point in time taking account of its temporal goals, norms and their conflicts. We propose a solution in which a normative planning problem is the basis for practical reasoning based on argumentation. Various types of conflict within goals, within norms and between goals and norms are identified based on temporal properties of these entities. The properties of the best plan(s) with respect to goal achievement and norm compliance are mapped to arguments, followed by mapping their conflicts to attack between arguments, all of which are used to identify why a plan is justified.

Controlling Interactions with Libraries in Android Apps through Runtime Enforcement

Android applications are executed on smartphones equipped with a variety of resources that must be properly accessed and controlled, otherwise the correctness of the executions and the stability of the entire environment might be negatively affected. For example, apps must properly acquire, use, and release microphones, cameras, and other multimedia devices otherwise the behaviour of the apps that use the same resources might be compromised. In this paper, we present an approach that lets users protect their environment from the apps that use resources improperly by enforcing the correct usage. This is achieved by using software enforcers that can observe executions and change them when necessary. For instance, enforcers can detect that a resource has been acquired but not released, and automatically perform the release operation, thus giving the possibility to use that same resource to the other apps. We call the software libraries augmented with one or more enforcers proactive libraries because the activation of the enforcer decorates the library with proactive behaviours that can guarantee the correctness of the execution despite the invocation of the operations implemented by the library. Experimental results reveal that proactive libraries are able to effectively correct misuse of resources with negligible runtime overheads.

Human-centric Data Dissemination in the IoP: Large-scale Modeling and Evaluation

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed "Internet of People" paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this paper, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behavior in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modeling methodology, which combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and country-wide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

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