Context: The adoption of Artificial Intelligence (AI) in industrial production systems has raised significant expectations for increased efficiency and innovation. Nevertheless, challenges such as the distributed nature of industrial operations, the heterogeneity of physical devices, and the complexity of real-world processes continue to hinder AI integration. Digital Twins (DTs) have emerged as a promising abstraction to decouple physical complexity from digital representations, facilitating more effective system management. Objective: This work investigates how AI can be systematically integrated with DTs in industrial contexts. The goal is to identify and characterize a set of interaction patterns that leverage the complementary strengths of AI and DTs to enhance industrial intelligence and performance. Methods: Drawing on a structured view of how responsibilities can be shared between AI technologies and DT-enabled shop floors, the paper defines four interaction patterns—AI Observing DTs, AI Advising DTs, AI Controlling DTs, and AI Embedded in DT. Each pattern is analyzed in terms of its roles, data and control flows, and typical application scenarios, and is illustrated on a DT-enabled physical micro-factory that reproduces realistic production conditions. Results: The four patterns show how different placements and responsibilities of AI components with respect to DT layers impact modularity, reuse of AI models, maintainability, and integration with legacy industrial systems. The micro-factory illustration highlights how the patterns can support practical use cases, including root-cause analysis of performance degradation, machine-level health monitoring, and AI-based production scheduling. Conclusion: Structuring AI–DT integration around interaction patterns provides a concrete way to bridge the gap between conceptual opportunities and operational industrial systems. The proposed patterns offer a reusable design vocabulary for positioning AI with respect to DT layers in cyber–physical production systems, and for reasoning about the architectural trade-offs of alternative integration strategies.

A defining feature of a Digital Twin (DT) is its level of ”entanglement”: the degree of strength to which the twin is interconnected with its physical counterpart. Despite its importance, this characteristic has not been yet fully investigated, and its impact on applications' design is underestimated. In this paper, we define the concept of “Degree of Entanglement” (DoE), which provides an operational model for assessing the strength of the entanglement between a DT and its physical counterpart. We also propose an interoperable representation of DoE within the Web of Things (WoT) framework, which enables DT-driven applications to dynamically adapt to changes in the physical environment. We evaluate our proposal using two realistic use cases, demonstrating the practical utility of DoE in supporting, for instance, context-awareness decisions and adaptiveness.

Chatbot-based systems are recognised in literature as an effective tool to support chronic diseases self-management. However, the core of most of this work is on the description of the application domain and on the motivations behind the adoption of recommendation systems that exploit chatbot to mediate the interaction with users, but they fail in providing sufficient details on the system architecture and on the technology adopted. Moreover, they are usually designed with a strong focus on the specific pathology, and a reference architectural solution that can be adopted in different contexts is missing, thus making the work useful only in the domain it is devised for. In this paper we provide a framework for developing recommendation systems based on chatbots that is meant to be applied in different scenarios. The framework is composed by a back-end recommendation engine that autonomously computes the user's adherence profile to prescription, and proactively provides motivational feedback to the user through the application front-end based on a chatbot. The chatbot is also meant to collect and aggregate data for profiling the individual health and habits. To demonstrate the feasibility of our framework, we present a recommendation system, based on a Telegram chatbot, that has been developed and trained for managing hypertensive patients.

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is "fluid", since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework, and prove it is self-stabilising and locally optimal. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling while maintaining a tuneable trade-off between accuracy and efficiency.

The increasing complexity and unpredictability of many ICT scenarios will represent a major challenge for future intelligent systems.The capability to dynamically and autonomously adapt to evolving and novel situations, with a partial or limited knowledge of the domain, both at the level of individual components and at the collective level, will become a crucial need for smart devices acting in many application domains.In this paper, we envision future systems able to selfdevelop mental models of themselves and of the environment they act in.Key properties will include: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act in the presence of others, i.e., at the collective level.In our work, we will introduce the vision of self-development in ICT systems, by framing its key concepts and by illustrating suitable application domains.Then, we overview the many research areas that are contributing or can potentially contribute to the realisation of the vision, and identify some key research challenges.

In recent years, digital twins have been pervading different application domains—from manufacturing to healthcare—as an approach for virtualising different kinds of physical entities (things, products, machines). The dominant view developed in the literature so far is about the virtualisation of individual physical assets in a closed-system perspective. In this article, we introduce and explore a broader perspective that we call Web of Digital Twins (WoDT), in which the digital twin paradigm is exploited for the pervasive softwarisation of possibly large-scale interrelated physical realities. A WoDT can be conceived as an open, distributed and dynamic ecosystem of connected digital twins, functioning as an interoperable service-oriented layer for applications running on top, especially smart applications and multiagent systems. The article introduces an abstract model and architecture aimed to capture key aspects of the idea not bound to any specific application domains or implementing technologies and discusses their adoption in engineering real-world systems. To this purpose, two concrete case studies are considered, in the context of healthcare and smart mobility. Finally, the article includes a discussion of a selected set of research directions.

Our streets will be soon populated by multitudes of autonomous (i.e., self-driving) vehicles, calling for appropriate solutions to coordinate their collective movements in order to ensure safety and efficiency. In particular, crossing intersections can be based on a number of different coordination approaches, from traditional ones (e.g., traffic lights) to innovative ones (e.g., based on dynamic negotiations between vehicles). In this paper, after having introduced the general issues associated to intersection management in the presence of autonomous vehicles, we show by simulation experiments that no single approach exhibits the best behaviour for all traffic conditions and for all performance indicators. On this basis, we introduce an adaptation mechanism that enables an intersection to dynamically select the most proper coordination approach depending on traffic conditions and the performance indicator to be optimized. Simulation experiments show the effectiveness of such adaptive approach.

Digital Twins (DTs) are becoming a reference design abstraction for many Internet of Things (IoT) application scenarios. Also, data processing is shifting to a decentralised setting leveraging the edge computing paradigm to move computation closer to the physical devices. In this context, Multi-access Edge Computing (MEC) technologies on 5G cellular networks are redefining the IoT networking infrastructure by enabling ultra low latency, and reliable and responsive connectivity. However, evaluation of the MEC architecture from the application developer standpoint is currently missing from literature, as well as an assessment of performance while adopting DT on top of MEC. Therefore, this paper reports on a MEC implementation based on OpenNESS toolkit, in the context of DT-based mobility, and an evaluation of its service-level performance.

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in "space and time" of distributed data structures called fields. More specifically regarding time, in field-based coordination (as in many other distributed approaches to coordination) it is assumed that local activities in each device are regulated by a fair and unsynchronised fixed clock working at the platform level. In this work, we challenge this assumption, and propose an alternative approach where scheduling is programmed in a natural way (along with usual field-based coordination) in terms of causality fields, each enacting a programmable distributed notion of a computation "cause" (why and when a field computation has to be locally computed) and how it should change across time and space. Starting from low-level platform triggers, such causality fields can be organised into multiple layers, up to high-level, collectively-computed time abstractions, to be used at the application level. This reinterpretation of time in terms of articulated causality relations allows us to express what we call "time-fluid" coordination, where scheduling can be finely tuned so as to select the triggers to react to, generally allowing to adaptively balance performance (system reactivity) and cost (resource usage) of computations. We formalise the proposed scheduling framework for field-based coordination in the context of the field calculus, discuss an implementation in the aggregate computing framework, and finally evaluate the approach via simulation on several case studies.

Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support System assisting clinicians in assessing health risk of patients by delivering "Risk Prediction as a Service". By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, and seamless interoperability, while promoting collaboration.

Edge Computing is rapidly gaining traction in scenarios such as Cyber-Physical Systems and Web of Things. Whereas the Cloud hides heterogeneity of devices behind its standard interfaces and protocols, the Edge should deal with it, as well as with embracing openness and governing interactions. In this paper we propose TuSoW as a model and technology for bringing tuple-based coordination to the Edge.

Distributing situated intelligence in Cyber-Physical Systems (CPS) to realise the vision of Internet of Intelligent Things (IoIT) raises issues of efficiency and scalability-in particular when dealing with huge numbers of physical objects. Such issues do not just regard the application or service logic and runtime, but also impact on the software development process. Moving from the notion of Logic Programming as a Service (LPaaS) - a re-interpretation of distributed logic programming tailored to the IoT era - in this paper we describe how its architecture and development process deals with the aforementioned issues from a software engineering standpoint, by discussing the design, development practices, and delivery means of the LPaaS technology.

Blockchain technologies are rapidly gaining attention in the multi-agent systems (MAS) community to face critical issues such as trust, secured communications, and data consistency. In particular, the notion of smart contract can be exploited to deploy trustworthy computations automatically executed by the network in a consistent way. MAS coordination - modelling and engineering of agents interaction in a MAS - thus represents an appealing application field for smart contracts, potentially enabling fully-decentralised, trustworthy coordination. Along this line, we focus on the Ethereum blockchain technology, map it onto LINDA tuple-based coordination model, and discuss two proof-of-concept implementations of LINDA on Ethereum. We hence demonstrate conceptual and technical feasibility of blockchain-based coordination in MAS, while emphasising issues of applying the blockchain beyond accountability and identity management.

Abstract We introduce Spatial Tuples, an extension of the basic tuple‐based model for distributed multi‐agent system coordination where (a) tuples are conceptually placed in regions of the physical world and possibly move anchored to a mobile computational device, (b) the behaviour of standard Linda coordination primitives is extended so as to depend on the spatial properties of the coordinating agents, tuples, and the topology of space, and (c) the tuple space can be conceived as a virtual layer augmenting physical reality. Motivated by the needs of mobile augmented‐reality applications, Spatial Tuples explicitly aims at supporting space‐aware and space‐based coordination in agent‐based pervasive computing scenarios. This paper presents the coordination model, its formalization as a process algebra, a library of patterns of coordination it enables, and a discussion of application scenarios, challenges, and open issues for future works.

The crucial role played by social interactions between smart objects in the Internet of Things (IoT) is being rapidly recognized by the social IoT (SIoT) vision. In this paper, we build upon the recently introduced vision of Speaking Objects-“things” interacting through argumentation-to show how different forms of human dialogue naturally fit cooperation and coordination requirements of the SIoT. In particular, we show how Speaking Objects can exchange arguments in order to seek for information, negotiate over an issue, persuade others, deliberate actions, and so on, namely, striving to reach consensus about the state of affairs and their goals. In this context, we illustrate how argumentation naturally enables such a form of conversational coordination through practical examples and a case study scenario.

Abstract New generations of distributed systems are opening novel perspectives for logic programming (LP): On the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper, we introduce the notion ofLogic Programming as a Service(LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First, we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.

In this paper we sketch a vision of future environments densely populated by smart sensors and actuators - possibly embedded in everyday objects - that, rather than simply producing streams of data, are capable of understanding and reporting, via factual assertions and arguments, about what is happening (for sensors) and about what they can make possibly happen (for actuators). These "speaking objects" form the nodes of a dense distributed computing infrastructure that can be exploited to monitor and control activities in our everyday environment. However, the nature of speaking objects will dramatically change the approaches to implementing and coordinating the activities of distributed processes. In fact, distributed coordination is likely to become associated with the capability of argumenting about situations and about the current "state of the affairs", with the aim of triggering and directing proper distributed "conversations" to collectively reach a future desirable state. Accordingly, we discuss how such a novel vision can build upon some readily available technologies, and the research challenges that it poses. Two case studies are used as exemplary scenarios.