In many AI applications, one has incomplete qualitative knowledge about the order of occurring events. A common way to express knowledge about this temporal reasoning problem is Allen’s interval algebra. Unfortunately, its main interesting reasoning tasks, consistency check and minimal labeling, are intractable (assuming P ≠ NP). Mostly, reasoning tasks in tractable subclasses of Allen’s algebra are performed with constraint propagation techniques. This paper presents a new reasoning approach that performs the main reasoning tasks much more efficient than traditional constraint propagation methods. In particular, we present a sound and complete O(n2)-time algorithm for minimal labeling computation that can be used for the pointisable subclass of Allen’s algebra.
CITATION STYLE
Kahl, J., Hotz, L., Milde, H., & Wessel, S. (1999). A more efficient knowledge representation for Allen’s algebra and point algebra. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1611, pp. 747–752). Springer Verlag. https://doi.org/10.1007/978-3-540-48765-4_79
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