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System-Level Use of Contextual Information

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Context-Enhanced Information Fusion

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

A system that exploits information—e.g. to support decision making—can use contextual information both in providing expectations and in resolving uncertain inferences. In the latter case, contextual reasoning involves inferring desired information (values of “problem variables”) on the basis of other available information (“context variables”). Relevant contexts are often not self-evident, but must be discovered or selected as a means to problem-solving. Therefore, context exploitation involves (a) predicting the value of contextual information to meet information needs; (b) selecting information types and sources expected to provide information useful in meeting those needs; (c) determining the relevance and quality of acquired information; and (d) applying selected information to a problem at hand. Fusion of contextual information can improve the quality of inferences, but involves concerns about the quality of the contextual information. The availability and quality of predictive models dictate the ways in which contextual information can be used. Many applications are benefitted by inference systems that adaptively discover and exploit context and refine such models to meet evolving information states and information needs.

The original version of this chapter was revised: Order of the names of authors was changed to Alan N. Steinberg and Galina L. Rogova. The erratum to this chapter is available at DOI 10.1007/978-3-319-28971-7_26

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-28971-7_26

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Notes

  1. 1.

    In Sect. 7.5.2 we distinguish four categories of reasoning. Anomaly-based detection is Category 1.

  2. 2.

    We will use the term ‘target’ throughout this chapter to refer to any real, perceived or postulated entity of interest at any level of abstraction: a feature, signal, individual, attribute, event, relationship, aggregation, process, concept, etc.

  3. 3.

    In [10] we examine these traditional fusion levels derived from the JDL Data Fusion Model, suggesting that the partitionings are not clear-cut. In particular, the dissimilarity of the L4 → L3 flow relative to other inter-level flows indicates that L4 does not obey the same partitioning criteria as the other levels. Additionally, the example state variables listed in Table 7.1 suggest that “level 4” fusion issues are the same as those for the other levels, but applied reflexively to the particular system doing the fusing.

  4. 4.

    We discuss abduction in Sect. 7.5.2 as Category 2 reasoning.

  5. 5.

    It might be useful to add yet another category (perhaps Category-1) to encompass estimation refinement via filtering or smoothing in the absence of a model; e.g., without model-driven filter gains.

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Author information

Authors and Affiliations

  1. 2568 Fox Ridge Ct, Woodbridge, VA, 22192, USA

    Alan N. Steinberg

  2. State University New York at Buffalo, Buffalo, USA

    Galina L. Rogova

Authors
  1. Alan N. Steinberg
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  2. Galina L. Rogova
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Corresponding author

Correspondence to Alan N. Steinberg .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Steinberg, A.N., Rogova, G.L. (2016). System-Level Use of Contextual Information. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28969-4

  • Online ISBN: 978-3-319-28971-7

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