Towards a general framework for information fusion

Didier Dubois, Weiru Liu, Jianbing Ma, Henri Prade

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
148 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence
Subtitle of host publication10th International Conference, MDAI 2013, Barcelona, Spain, November 20-22, 2013. Proceedings
EditorsVicenç Torra, Yasuo Narukawa, Guillermo Navarro-Arribas, David Megías
PublisherSpringer
Pages37-48
Number of pages12
ISBN (Electronic)9783642415500
ISBN (Print)9783642415494
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer-Verlag
Volume8234
ISSN (Print)0302-9743

Structured keywords

  • Jean Golding

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  • Cite this

    Dubois, D., Liu, W., Ma, J., & Prade, H. (2013). Towards a general framework for information fusion. In V. Torra, Y. Narukawa, G. Navarro-Arribas, & D. Megías (Eds.), Modeling Decisions for Artificial Intelligence: 10th International Conference, MDAI 2013, Barcelona, Spain, November 20-22, 2013. Proceedings (pp. 37-48). (Lecture Notes in Computer Science; Vol. 8234). Springer. https://doi.org/10.1007/978-3-642-41550-0_4