Preference Representation with Weighted Goals: Expressivity, Succinctness, Complexity
Joel Uckelman, Ulle Endriss
Abstract:
Preference Representation with Weighted Goals:
Expressivity, Succinctness, Complexity
Joel Uckelman, Ulle Endriss
Abstract:
The representation of preferences of agents is a central feature in
many AI systems. In particular when the number of alternatives to be
considered may become large, the use of compact preference
representation languages is crucial. The framework of weighted
propositional formulas can be used to define several such
languages. The central idea is to associate numerical weights with
goals specified in terms of propositional formulas, and to compute the
utility value of an alternative as the sum of the weights of the goals
it satisfies. In this paper, we analyze several properties of
languages defined by weighted goals: their expressivity, the relative
succinctness of different sublanguages, and the computational
complexity of finding the best alternative with respect to a given
utility function expressed in terms of weighted goals.