Michele Loreti

Matteo Belenchia, Flavio Corradini, Michela Quadrini et al.

Graph neural networks form a class of deep learning architectures specifically designed to work with graph-structured data. As such, they share the inherent limitations and problems of deep learning, especially regarding the issues of explainability and trustworthiness. We propose $μ\mathcal{G}$, an original domain-specific language for the specification of graph neural networks that aims to overcome these issues. The language's syntax is introduced, and its meaning is rigorously defined by a denotational semantics. An equivalent characterization in the form of an operational semantics is also provided and, together with a type system, is used to prove the type soundness of $μ\mathcal{G}$. We show how $μ\mathcal{G}$ programs can be represented in a more user-friendly graphical visualization, and provide examples of its generality by showing how it can be used to define some of the most popular graph neural network models, or to develop any custom graph processing application.

Maurice H. ter Beek, Michele Loreti

Collective Adaptive Systems (CAS) consist of a large number of spatially distributed heterogeneous entities with decentralised control and varying degrees of complex autonomous behaviour that may be competing for shared resources even when collaborating to reach common goals. It is important to carry out thorough quantitative modelling and analysis and verification of their design to investigate all aspects of their behaviour before they are put into operation. This requires combinations of formal methods and applied mathematics which moreover scale to large-scale CAS. The primary goal of FORECAST is to raise awareness in the software engineering and formal methods communities of the particularities of CAS and the design and control problems which they bring.

Diego Latella, Michele Loreti, Mieke Massink et al.

Predicate-based communication allows components of a system to send messages and requests to ensembles of components that are determined at execution time through the evaluation of a predicate, in a multicast fashion. Predicate-based communication can greatly simplify the programming of autonomous and adaptive systems. We present a stochastically timed extension of the Software Component Ensemble Language (SCEL) that was introduced in previous work. Such an extension raises a number of non-trivial design and formal semantics issues with different options as possible solutions at different levels of abstraction. We discuss four of these options, of which two in more detail. We provide a formal semantics definition and an illustration of the use of the language modeling a bike sharing system, together with some preliminary analysis of the system performance.