The field of logic-based approaches for dynamic systems is witnessing significant developments, with a focus on enhancing the expressiveness and efficiency of existing frameworks. Researchers are exploring novel extensions and combinations of formal methods, such as Answer Set Programming (ASP) and modal logics, to tackle complex problems in areas like cloud-edge infrastructure, operating room scheduling, and temporal reasoning.

Notable trends include the integration of machine learning techniques with logic-based approaches to improve prediction accuracy and robustness, as well as the development of new semantics for disjunction in ASP. Additionally, there is a growing interest in applying logic-based methods to real-world problems, such as elective surgery rescheduling and resource utilization optimization.

Some noteworthy papers in this area include: Application Placement with Constraint Relaxation, which exploits Answer Set Programming optimisation capabilities to tackle combinatorial optimisation problems. Towards Constraint Temporal Answer Set Programming, which introduces a novel temporal and constraint-based extension of the logic of Here-and-There, representing a significant advancement in nonmonotonic temporal reasoning with constraints. Improving ASP-based ORS Schedules through Machine Learning Predictions, which integrates inductive and deductive techniques to generate provisional schedules and update the encoding correspondingly. Automated Hybrid Grounding Using Structural and Data-Driven Heuristics, which develops an automated hybrid grounding approach that combines the strength of standard bottom-up grounding with body-decoupled grounding. Comparing Non-minimal Semantics for Disjunction in Answer Set Programming, which compares different semantics for disjunction in ASP that do not adhere to the principle of model minimality and proves that three of these approaches actually coincide.