###### Abstract  
Incremental techniques aim at making it possible to improve the performance of the grounding and solving processes by reusing the results of previous executions. Clingo supports both incremental grounding and incremental solving computations. In order to leverage incremental computations in clingo, the incremental fragments of ASP programs must satisfy certain safety-related conditions. In a number of problem domains and reasoning tasks, these conditions can be satisfied in a fairly straightforward way. However, we have observed that in certain practical applications, satisfying the conditions becomes more challenging, to the point that it is sometimes unclear how or even if it is possible to leverage incremental computations. In this paper, we report our findings, and ultimate success, with the use of incremental grounding and solving techniques in one of these challenging cases. We describe the domain, which is linked to a large practical application, discuss the challenges we faced in attempting to leverage incremental computations, and then describe the techniques that we developed, in particular at the level of methods for encoding the domain knowledge and of algorithms supporting the intended interleaving of grounding and solving. We believe that our findings may provide valuable information to practitioners facing similar challenges and ultimately increase the adoption of clingo's incremental capabilities for complex practical applications. a _algorithms_2023, 16, 169. [[https://doi.org/10.3390/a16030169](https://doi.org/10.3390/a16030169)]([https://doi.org/10.3390/a16030169](https://doi.org/10.3390/a16030169))
the use of ASP for such a task is not new, our situation was complicated by a number of factors. At its core, our application [13; 14] formalized and reasoned about policies [15] aimed at determining an individual's readiness to perform activities (work, school, sports, etc.) during the peak of the COVID-19 pandemic. At that time, the task of easing COVID-19 lockdown restrictions was complicated due to a variety of factors, including the uncertainty about the way in which the virus spread and the challenges in adapting pre-existing environments that were not meant to limit the spread of such an aggressive airborne virus. A practice commonly used with the aim of reducing risks consisted in adopting policies that determined an individual's fitness to access an environment or perform an activity. For instance, for an employee to be granted access an office environment on a given day, the person may be required to have been symptom-free for the past 24 h and not have been in contact with sick individuals for the past 7 days. A student returning to campus from another state may be required to have tested negative for COVID-19 in the past 5 days or he/she would have to quarantine for 14 days; then again, in some policies it was possible to shorten such quarantines under certain very specific conditions (e.g., a particular battery of tests providing the necessary results), but it was also possible that the quarantine would need to be extended if other conditions became true (e.g., having certain symptoms). Policies often became very complex to both describe and understand. Due to how rapidly conditions and recommendations were changing, and to the many ramifications of resuming regular activities or not, it was paramount to have tools that simplified the task of developing and enforcing policies. Particularly, it was essential to enable (1) the rapid development of policies, (2) trust that the implementation of the policies reflected the original intent, and (3) transparency of the decisions made by such implementations.