Preference modeling by weighted goals with max aggregation

Logic-based preference representation languages are promising for expressing preferences over combinatorial domains. Sets of weighted formulas, called goalbases, can be used to define several such languages. How goalbases are translated into utility functions---that is, by what aggregation function this is done---is a crucial component of this type of language. In this paper, we consider the properties of several goalbase languages which use max as their aggregation function. In particular, we examine the expressivity, succinctness and complexity of such languages

Preference modeling by weighted goals with max aggregation, in

Logic-based preference representation languages are promising for expressing preferences over combinatorial domains. Sets of weighted formulas, called goalbases, can be used to define several such languages. How goalbases are translated into utility functions—that is, by what aggregation function this is done—is a crucial component of this type of language. In this paper, we consider the properties of several goalbase languages which use max as their aggregation function. In particular, we examine the expressivity, succinctness and complexity of such languages

Preference modeling by weighted goals with max aggregation, in:

Abstract Logic-based preference representation languages are promising for expressing preferences over combinatorial domains. Sets of weighted formulas, called goalbases, can be used to define several such languages. How goalbases are translated into utility functions-that is, by what aggregation function this is done-is a crucial component of this type of language. In this paper, we consider the properties of several goalbase languages which use max as their aggregation function. In particular, we examine the expressivity, succinctness and complexity of such languages