Rafael Peñaloza
Rafael Peñaloza is an Associate Professor at the University of Milano-Bicocca. His main interests lie within the area of symbolic AI, specifically in knowledge representation and reasoning, where he studies how to deal with imperfect knowledge; i.e., imprecisions, inconsistencies, uncertainty, preferences, and more, mainly connected with description logics and temporal logics. He has coordinated several national and international projects and is now part of the Department of Excellence DISCo in Milano-Bicocca, and of the FAIR project AMAR.
Invited Talk - Rough Knowledge and its Refinement
We often learn concepts through refinement. From a rough general approximation, more details are observed and captured, and general concepts are partitioned into more specific classes. Based on this insight, we study rough description logics with a hierarchy of equivalence refinements. We show how to reason with this logic and how to navigate between levels of detail.
Stefano Bistarelli
TBD
Giuseppe Mazzotta
Giuseppe Mazzotta is a Post-Doctoral Researcher in Computer Science at the Department of Mathematics, University of Calabria, Italy. He obtained his MSc Degree in Computer Science in 2020 and completed his PhD in Computer Science and Mathematics in 2023 at the same institution. Giuseppe’s research focuses on knowledge representation and reasoning, particularly in Answer Set Programming (ASP). During his PhD, he specialized in developing efficient techniques for evaluating ASP programs affected by the grounding bottleneck problem. His work has been published in international conferences, earning him recognition such as the “AAAI Outstanding Student Paper Honorable Mention” at the 36th AAAI Conference on Artificial Intelligence.
Invited Tutorial - ASP with Quantifiers: A Natural and Efficient Way to Tackle Problems Beyond NP
The success of Answer Set Programming (ASP) stems from its highly expressive language, capable of modeling complex combinatorial problems, and from the availability of efficient solvers that make ASP practical in real-world scenarios. However, despite these strengths, the expressiveness of ASP is inherently limited to the second level of the Polynomial Hierarchy (PH). As a result, a wide range of problems that go beyond this complexity class cannot be modeled in ASP. To address this limitation, Answer Set Programming with Quantifiers (ASP(Q)) has been proposed. ASP(Q) extends the ASP language with the ability to quantify over answer sets, enabling a natural modeling of problems across the entire PH.
In this tutorial, we explore the ASP(Q) formalism along the two dimensions that have driven the success of ASP: modeling capabilities and efficient solving. First, we will demonstrate how ASP(Q) allows for natural and intuitive modeling of several hard (optimization) problems of practical relevance. Then, we will turn our attention to the efficient evaluation of ASP(Q) programs. Specifically, we will introduce the PyQASP system, which compiles ASP(Q) programs into compact and optimized Quantified Boolean Formulae (QBF), allowing them to be evaluated effectively using well-established and mature QBF technologies.
Through this tutorial, attendees will gain a comprehensive overview of the ASP(Q) formalism and how it can be applied to model and solve problems beyond NP across a variety of practical domains.