Recently forms of abstraction have been proposed for both logic programs (LPs) under the answer set semantics (ASP) and for the related formalism of assumption-based argumentation (ABA), e.g., via clustering of atoms or assumptions, in order to simplify a given LP or ABA framework. In both approaches after clustering the original answer sets and assumption sets are over-approximated, with the aim of avoiding spuriousness. In contrast, in ASP a given LP is syntactically modified to achieve over-approximation, while on ABA the framework is minimally modified and the semantics is abstracted. In this work we follow the latter approach and provide a novel semantical abstraction for LPs and for ABA frameworks corresponding to LPs.
Weather forecasting is important for saving lives, protecting property, and supporting economic activities. It provides timely warnings for severe weather, improves agricultural planning, and aids in disaster management. Neural networks and deep learning methods can achieve impressive accuracy in weather prediction, but their black-box nature lacks in explainability. To address this limitation, we investigated the potential of FastLAS, an Inductive Logic Programming (ILP) framework, to produce reliable and, more important, explainable weather predictions. FastLAS learns ASP programs whose syntax and structural semantics resemble natural human language, making them easily understandable and interpretable by humans. The supportedness of stable models allows a clear explanation of the predictions. Our empirical evaluation on data from an Italian weather forecasting center shows that our approach is capable of learning predictive models from small dataset (a few samples instead of the thousands needed by neural networks) achieving an accuracy higher than statistical machine learning base lines.
Large Language Models (LLMs) lack the ability for commonsense reasoning and learning from text. In this work, we present a system, called LLM2LAS, for learning commonsense knowledge from story-based question and answering expressed in natural language. LLM2LAS combines the semantic parsing capability of LLMs with ILASP for learning commonsense knowledge expressed as answer set programs. LLM2LAS requires only few examples of questions and answers to learn general commonsense knowledge and correctly answer unseen questions. An empirical evaluation demonstrates the viability of our approach.
In this paper, we present the integration of MiniZinc into ASP Chef, expanding its capabilities to include constraint programming alongside Answer Set Programming (ASP). By leveraging the web assembly version of MiniZinc, this integration allows for running MiniZinc models directly in the browser, eliminating the need for additional software installations. This browser-based approach is particularly advantageous for educational settings and rapid prototyping, offering a seamless and accessible environment for learners and practitioners. To facilitate the incorporation of MiniZinc in ASP recipes, we have implemented a mapping mechanism that converts facts to MiniZinc data and vice versa. This integration not only broadens the scope of problems that can be addressed using ASP Chef but also simplifies the workflow for users, making it a versatile tool for complex computational tasks.
We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constrains, like durations and deadlines. A central challenge is to maintain scalability when dealing with fine-grained timing constraints, which can significantly exacerbate ASP's grounding bottleneck. To address this issue, we leverage extensions of ASP with difference constraints, a simplified form of linear constraints, to handle time-related aspects externally. Our approach effectively decouples metric ASP from the granularity of time, resulting in a solution that is unaffected by time precision.
