a problem that sometimes occurs in multiobjective optimization is the existence of a large set of fairly effcient solutions. hence, the decision making based on selecting a unique preferred solution is diffcult. considering models with fair b-effciency relieves some of the burden from the decision maker by shrinking the solution set, since the set of fairly b-efficient solutions is contained with...

Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing ...

CP-Nets are a framework for dealing with qualitative preferences, both conditional and unconditional. They have received a lot of attention recently, and many extensions have been provided. In particular, the framework of constrained CP-Nets aims to choose, amongst the solutions that satisfy a set of constraints, the preferred one. While the semantics of CP-Nets allows for cycles (and, indeed, ...

Multiagent Learning (MAL) is the algorithmic study of learning in a group of two or more agents. If the agents are based on different algorithms, and if there is no form of prior coordination between the agents, then this is called an ad hoc team problem [48]. Following a literature review [2] and a research proposal [1], the work at hand compares the performance of five MAL algorithms in ad ho...