About: Management of uncertainty orderings through ASP

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Management of uncertainty orderings through ASP
Creator	Capotorti, Andrea Formisano, Andrea
Source	Elsevier; PMC
abstract	Abstract 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.
has issue date	2006-12-31(xsd:dateTime)
bibo:doi	10.1016/b978-044452075-3/50012-1
has license	els-covid
sha1sum (hex)	93185810db7edfb5e3ecab72eee8a1f6b2ecd3f7
schema:url	https://doi.org/10.1016/b978-044452075-3/50012-1
resource representing a document's title	Management of uncertainty orderings through ASP
has PubMed Central identifier	PMC7151761
schema:publication	Modern Information Processing
resource representing a document's body	covid:93185810db7edfb5e3ecab72eee8a1f6b2ecd3f7#body_text
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