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  1. The Space Domain Ontologies.Alexander P. Cox, C. K. Nebelecky, R. Rudnicki, W. A. Tagliaferri, J. L. Crassidis & B. Smith - 2021 - In National Symposium on Sensor & Data Fusion Committee.
    Achieving space situational awareness requires, at a minimum, the identification, characterization, and tracking of space objects. Leveraging the resultant space object data for purposes such as hostile threat assessment, object identification, and conjunction assessment presents major challenges. This is in part because in characterizing space objects we reference a variety of identifiers, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, operational processes, operational statuses, and so forth, which tend to be defined in highly heterogeneous and sometimes inconsistent (...)
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  • Conceptual Space Modeling for Space Event Characterization.Jeremy R. Chapman, David Kasmier, David Limbaugh, Stephen R. Gagnon, John L. Crassidis, James Llinas, Barry Smith & Alexander P. Cox - 2020 - IEEE 23rd International Conference on Information Fusion (FUSION).
    This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data is (...)
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  • Computer Science as Empirical Inquiry: Symbols and Search.Allen Newell & H. A. Simon - 1976 - Communications of the Acm 19:113-126.
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  • Conceptual spaces as a framework for knowledge representation.Peter Gardenfors - 2004 - Mind and Matter 2 (2):9-27.
    The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non- symbolic models. Conceptual spaces consist of a number of 'quality dimensions' that often are (...)
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