Proposition and learning of some belief function contextual correction mechanisms *

Frédéric Pichon (1) , David Mercier (1) , Eric Lefevre (1) , François Delmotte (1)

1 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois

Knowledge about the quality of a source can take several forms: it may for instance relate to its truthfulness or to its relevance, and may even be uncertain. Of particular interest in this paper is that such knowledge may also be contextual; for instance the reliability of a sensor may be known to depend on the actual object observed. Various tools, called correction mechanisms, have been developed within the theory of belief functions, to take into account knowledge about the quality of a source. Yet, only a single tool is available to account for contextual knowledge about the quality of a source, and precisely about the relevance of a source. There is thus some lack of flexibility since contextual knowledge about the quality of a source does not have to be restricted to its relevance. The first aim of this paper is thus to try and enlarge the set of tools available in belief function theory to deal with contextual knowledge about source quality. This aim is achieved by (1) providing an interpretation to each one of two contextual correction mechanisms introduced initially from purely formal considerations, and (2) deriving extensions-essentially by uncovering contextual forms-of two interesting and non contextual correction mechanisms. The second aim of this paper is related to the origin of contextual knowledge about the quality of a source: due to the lack of dedicated approaches, it is indeed not clear how to obtain such specific knowledge in practice. A sound, easy to interpret and computationally simple method is therefore provided to learn from data contextual knowledge associated with the contextual correction mechanisms studied in this paper.

Mots clés: Dempster-Shafer theory; Belief functions; Information correction; Discounting

Domaines: Intelligence artificielle [cs.AI]

Frédéric Pichon, David Mercier, Eric Lefevre, François Delmotte. Proposition and learning of some belief function contextual correction mechanisms *. International Journal of Approximate Reasoning, 2016, 72, pp.4-42. ⟨10.1016/j.ijar.2015.12.012⟩. ⟨hal-03354123⟩