In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".
Q: Paragraph: Traditionally, most of the proposed probabilistic models of decision under uncertainty rely on numerical measures and representations. Alternative proposals call for qualitative (non-numerical) treatment of uncertainty, based on preference relations and belief orders. The automation of both numerical and non-numerical frameworks surely represents a preliminary step in the development of inference engines of intelligent agents, expert systems, and decision-support tools. In this paper we exploit Answer Set Programming to formalize and reason about uncertainty expressed as belief orders. The availability of ASP-solvers supports the design of automated tools to handle such formalizations. Our proposal reveals particularly suitable whenever the domain of discernment is partial. We first illustrate how to automatically "classify ^^, according to the most well-known uncertainty frameworks, any given qualitative uncertainty assessment. Then, we show how to compute an enlargement of the assessment, to any other new inference target.
Title: Management of uncertainty orderings through ASP
A:
True