  Search-optimization problems are plentiful in scientific and engineering
domains. Artificial intelligence has long contributed to the development of
search algorithms and declarative programming languages geared toward solving
and modeling search-optimization problems. Automated reasoning and knowledge
representation are the subfields of AI that are particularly vested in these
developments. Many popular automated reasoning paradigms provide users with
languages supporting optimization statements: answer set programming or MaxSAT
on minone, to name a few. These paradigms vary significantly in their languages
and in the ways they express quality conditions on computed solutions. Here we
propose a unifying framework of so-called weight systems that eliminates
syntactic distinctions between paradigms and allows us to see essential
similarities and differences between optimization statements provided by
paradigms. This unifying outlook has significant simplifying and explanatory
potential in the studies of optimization and modularity in automated reasoning
and knowledge representation. It also supplies researchers with a convenient
tool for proving the formal properties of distinct frameworks; bridging these
frameworks; and facilitating the development of translational solvers.
