Toward a General Framework for Information Fusion

Didier Dubois (1, 2, 3) , Weiru Liu (2) , Jianbing Ma (4, 5) , Henri Prade (1, 3)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
2 QUB - Queen's University [Belfast]
3 CNRS - Centre National de la Recherche Scientifique
4 IRIT - Institut de recherche en informatique de Toulouse
5 BU - Bournemouth University [Poole]

Depending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory, the latter encompassing the two other ones as special cases. This unified discussion of combination rules across different settings is expected to provide a fresh look on some old but basic issues in information fusion.

Mots clés: Data Mining and Knowledge Discovery

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]; Informatique et langage [cs.CL]

Didier Dubois, Weiru Liu, Jianbing Ma, Henri Prade. Toward a General Framework for Information Fusion. 10th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2013), Nov 2013, Barcelone, Spain. pp.37-48, ⟨10.1007/978-3-642-41550-0_4⟩. ⟨hal-01212886⟩