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Knowledge Acquisition, Representation, Processing & Presentation (KARPP)

  • SUNY Buffalo
  • Los Alamos National Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Knowledge Acquisition, Representation, Processing, & Presentation (KARPP) is a new system level approach to the acquisition and aggregation of mission-valued and qualified situation knowledge for use by a decision maker. KARPP is decomposed into a service oriented architecture (SOA) of knowledge aggregation services (KAS) with well-defined inputs and outputs which are implemented in hidden processes. The transfer of knowledge from one KAS to another is through a qualified knowledge request (QKR) which results in an aggregated knowledge response (AKR). The notion of qualified knowledge is introduced to specify the knowledge that is needed by a requesting KAS as well as the knowledge quality attributes and the magnitude of those qualities which are required to meet mission needs. The goals of this approach are to provide an architectural framework for realizing managed, adaptive fusion processing capabilities, and to integrate the sensor/database control and data acquisition with the fusion-based data processing which will maximize the expected knowledge value rate (EKVR) provided to mission managers.

Original languageEnglish
Title of host publication2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-54
Number of pages8
ISBN (Electronic)9798350362817
DOIs
StatePublished - 2024
Event2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024 - Montreal, Canada
Duration: May 7 2024May 10 2024

Publication series

Name2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024

Conference

Conference2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024
Country/TerritoryCanada
CityMontreal
Period05/7/2405/10/24

Keywords

  • data fusion
  • expected information value rate
  • KARPP
  • knowledge aggregation
  • sensor management

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