Much like relational probabilistic models, the need for relational preference models arises naturally in real-world applications where the set of object classes is fixed, but object instances vary from one application to another as well as within the run-time of a single application. To address this problem, we suggest a rule-based preference specification language. This language extends regular rule-based languages and leads to a much more flexible approach for specifying control rules for autonomous systems. It also extends standard generalized-additive value functions to handle a dynamic universe of objects: given any specific set of objects it induces a generalized-additive value function. Throughout the paper we use the example of a decision support system for command and control centers we are currently developing to motivate the need for such models and to illustrate them.