We analyze combinatorial optimization problems with ordinal, i.e., non-additive, objective functions that assign categories (like good, medium and bad) rather than cost coefficients to the elements of feasible solutions. review different optimality concepts for ordinal discuss their similarities differences. then focus on two prevalent are shown be equivalent. Our main lies investigation a bije...

Multi-label classifiers allow us to predict the state of a set of responses using a single model. A multi-label model is able to make use of the correlation between the labels to potentially increase the accuracy of its prediction. Critical applications of multi-label classifiers (such as medical diagnoses) require that the system’s confidence in prediction also be provided with the multi-label...

In this paper, we introduce the COmbinatorial Multi-Objective Multi-Armed Bandit (COMOMAB) problem that captures the challenges of combinatorial and multi-objective online learning simultaneously. In this setting, the goal of the learner is to choose an action at each time, whose reward vector is a linear combination of the reward vectors of the arms in the action, to learn the set of super Par...

Consider a system with M robots each having K resource types and N tasks having a vector of Kresource types. A coalition scheme represents a grouping of a given set of robots into disjoint subsets where each subset (coalition ) is assigned one task. Thus for N tasks we have N coalition schemes, represented as CS = {C1, C2,,....CN}, In this problem we have considered the following two conflictin...