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Belief-Based Argumentation in Intelligence Analysis and Decision Making

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Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection (PAAMS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 616))

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Abstract

This paper asserts that a multi-perspective viewpoint must be taken in the design of a computational system support capability for decision-making. We offer views from a Decision-Science slant, a Systemic Architectural view, and the need for technological support to realize improvements in analytical rigor. We have been researching and evolving the design of an analysis tool framework exploiting the hybrid concepts of a Belief-based Argumentation and Story-based subsystem. The notion of rigor, defined as a quality measure on the reasoning/analysis process, is one overarching principle of our approach, driven by the need for the associated analysis/decision-support product quality that complex modern problems demand. Our approach to the design of a mixed-initiative analysis tool is highly multidisciplinary and has taken account of an exhaustive review of the relevant literature along each viewpoint.

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Notes

  1. 1.

    It is important to notice that uncertainty related to arguments based on soft and hard information are usually represented within the frameworks of the different uncertainty theories (probability, fuzzy, belief, etc.). One of the methods to deal with different uncertainty representations is to transform them to be expressed in terms of the TBM [20].

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Acknowledgements

This paper results from research supported by the U.S. Naval Postgraduate School Assistance Grant No. N00244-15-1-0051 awarded by the NAVSUP Fleet Logistics Center SanDiego (NAVSUP FLC San Diego). The views expressed in written materials or publications, and/or made by speakers, moderators, and presenters, do not necessarily reflect the official policies of the Naval Postgraduate School nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

Author information

Authors and Affiliations

  1. State University of New York at Buffalo, Buffalo, NY, USA

    James Llinas & Galina Rogova

Authors
  1. James Llinas
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  2. Galina Rogova
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Corresponding author

Correspondence to James Llinas .

Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  2. Universidad de Sevilla, Sevilla, Spain

    María José Escalona

  3. Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada

    Sylvain Giroux

  4. University of Poznan, Poznan, Poland

    Patrycja Hoffa-Dąbrowska

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Vicente Julián

  6. Department of Informatics, Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. Fluminense Federal University, Rio de Janeiro, Brazil

    Nayat Sánchez-Pi

  8. Inst. Comp. Sci. & Bus. Info. Sys. (ICB), University of Duisburg-Essen, Essen, Nordrhein-Westfalen, Germany

    Rainer Unland

  9. Departamento de Informática e Estatístic, Universidade Federal de Santa Catarina, Florianópolis S.C.,, Brazil

    Ricardo Azambuja-Silveira

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Llinas, J., Rogova, G. (2016). Belief-Based Argumentation in Intelligence Analysis and Decision Making. In: Bajo, J., et al. Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection. PAAMS 2016. Communications in Computer and Information Science, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-39387-2_28

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  • DOI: https://doi.org/10.1007/978-3-319-39387-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39386-5

  • Online ISBN: 978-3-319-39387-2

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