Kristian Reale

The increasing penetration of renewable generation and distributed storage requires scheduling methods that combine accurate prediction with flexible constraint modeling. This paper proposes a neurosymbolic framework for renewable energy community scheduling in which neural forecasting modules estimate photovoltaic production and building-level demand, and a symbolic optimization layer computes storage and grid-exchange decisions using Answer Set Programming with Linear Constraints. The framework separates data-driven prediction from declarative decision modeling: forecasts provide time-indexed exogenous parameters, while technical limits, storage dynamics, and operational policies are encoded as symbolic constraints. We evaluate the approach on real data collected at the University of Calabria under configurations combining measured and predicted production and consumption. The results show that the Answer Set Programming with Linear Constraints scheduler computes optimal day-ahead schedules with negligible runtime for 24-hour horizons and obtains the same energy-exchange optimum as a Mixed-Integer Linear Programming baseline in the considered cases. Beyond numerical performance, the symbolic formulation supports incremental model extension: additional operational requirements, such as limiting battery discharge events to mitigate degradation, can be expressed through compact declarative rules without redesigning the solver. These results indicate that Answer Set Programming with Linear Constraints is a suitable optimization layer for energy-community scheduling when operational requirements are expected to evolve over time.

In the last few years, we have witnessed the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. This work presents DLV-EE, a framework based on Answer Set Programming (ASP) for performing declarative reasoning tasks over data-intensive, distributed applications. It relies on the DLV2 system and it features interoperability means for dealing with Big-Data over modern industry-level databases (relational and NoSQL). Furthermore, the work introduces DLV-IoT, an ASP system compatible with “mobile” technologies for enabling advanced reasoning capabilities on smart/IoT devices; eventually, DLV-EE and DLV-IoT via some real-world applications are illustrated as well.

Answer set programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach allowing for interactive localization of bugs in non-ground programs. The new approach points the user directly to a set of non-ground rules involved in the bug, which might be refined (up to the point in which the bug is easily identified) by asking the programmer a sequence of questions on an expected answer set. The approach has been implemented on top of the ASP solver wasp. The resulting debugger has been complemented by a user-friendly graphical interface, and integrated in aspide, a rich integrated development environment (IDE) for answer set programs. In addition, an empirical analysis shows that the new debugger is not affected by the grounding blowup limiting the application of previous approaches based on meta-programming.