Tootfinder

Improving ASP-based ORS Schedules through Machine Learning Predictions
Pierangela Bruno, Carmine Dodaro, Giuseppe Galat\`a, Marco Maratea, Marco Mochi
https://arxiv.org/abs/2507.16454

Improving ASP-based ORS Schedules through Machine Learning Predictions
The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different department units. Recently, solutions to this problem based on Answer Set Programming (ASP) have been delivered. Such solutions are overall satisfying but, when applied to real data…

Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling
Ruanqianqian Huang, Ayana Monroe, Peli de Halleux, Sorin Lerner, Nikolaj Bj{\o}rner
https://arxiv.org/abs/2506.08294

Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling
Constraint-satisfaction problems (CSPs) are ubiquitous, ranging from budgeting for grocery shopping to verifying software behavior. Logic modeling helps solve CSPs programmatically using SMT solvers. Despite its importance in many Computer Science disciplines, resources for teaching and learning logic modeling are scarce and scattered, and challenges remain in designing educational environments for logic modeling that are accessible and meet the needs of teachers and students. This paper explor…