Despite the widespread success of pattern database (PDB) heuristics in classical planning, to date there has been no application of PDBs to planning with numeric variables. In this paper we attempt to close this gap. We address optimal numeric planning involving conditions characterized by linear expressions and actions that modify numeric variables by constant quantities. Building upon prior research, we present an adaptation of PDB heuristics to numeric planning, introducing several approaches to deal with the unbounded nature of numeric variable projections. These approaches aim to restrict the initially infinite projections, thereby bounding the number of states and ultimately constraining the resulting PDBs. We show that the PDB heuristics obtained with our approach can provide strong guidance for the search.

Personalized medication plans determine the selection, dosage, and administration schedule of medications, to achieve medical goals that are specific to the patient and to its individual health constraints. This paper introduces medication planning as a novel domain for artificial intelligence planning, using PDDL+. We evaluate the suggested representation via experiments based on data collected from medical studies conducted on mice and rats.

Organizational clusters are often shared among users who compete over resources, but the organization's goal is to increase the overall utility produced by the cluster. That is, to increase the aggregate benefit drawn from the cluster. To overcome this problem, we enhanced SLURM with an auctioning system. We evaluated our work on several real cluster traces. Our auctioning scheduler increases the responsiveness to highly valued jobs. It reduces the weight of the queued jobs (the sum of multiplication of jobs by their wait time by their number of required nodes and by their bid) by 3X--33X compared with backfilling.

Organizational clusters are often shared among users who compete over resources, but the organization's goal is to increase the overall utility gathered from the cluster. That is, to maximize the aggregate benefit drawn from the cluster. To overcome this problem, we enhanced SLURM with an auctioning system. We evaluated our work on several real cluster traces. Our auctioning scheduler reduces the weight of the queued jobs by 3X-33X compared with backfilling.