Abstract
Developing efficient approaches for reasoning under uncertainty is an important issue in many applications. Several graphical [3] and logical-based methods have been proposed to reason with incomplete information in various uncertainty theory frameworks. This paper focuses on possibility theory which is a convenient uncertainty theory framework to represent different kinds of prioritized pieces of information. It provides a brief overview of main compact representation formats, and their associated inference tools, that exist in a possibility theory framework.
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Benferhat, S. (2010). Graphical and Logical-Based Representations of Uncertain Information in a Possibility Theory Framework. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_3
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DOI: https://doi.org/10.1007/978-3-642-15951-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15950-3
Online ISBN: 978-3-642-15951-0
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