Abstract
Temporal abstraction methods produce high level descriptions of a parameter evolution from collections of temporal data. As the level of abstraction of the data is increased, it becomes easier to use them in a reasoning process based on high-level explicit knowledge. Furthermore, the volume of data to be treated is reduced and, subsequently, the reasoning becomes more efficient. Besides, there exist domains, such as medicine, in which there is some imprecision when describing the temporal location of data, especially when they are based on subjective observations. In this work, we describe how the use of fuzzy temporal constraint networks enables temporal imprecision to be considered in temporal abstraction.
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Campos, M., Juárez, J.M., Salort, J., Palma, J., Marín, R. (2007). Temporal Abstraction of States Through Fuzzy Temporal Constraint Networks. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_61
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DOI: https://doi.org/10.1007/978-3-540-73053-8_61
Publisher Name: Springer, Berlin, Heidelberg
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