Abstract
In Computer Science, especially in AI, the treatment of temporal information is very important. For example, temporal restrictions for actions play a central role in planning. Mostly, qualitative constraints between actions, i.e. between time intervals assigned to actions, are represented in so called time interval networks. But humans involved often specify inconsistent networks. Thus, to support these people in converting contradictory networks into contradiction-free ones, a new efficient method is proposed.
The discussed method directly searches for solutions of the contradictions in the user-defined networks. Thereby, consistent time interval networks are constructed, so called elementary solutions which contain a maximal number of user-defined assumptions.
In this paper, a basic algorithm for computing elementary solutions is presented, and for reasons of efficiency a modification of this algorithm is explained. In this way, we get an efficient computation of elementary solutions for contradictory time interval networks.
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© 1993 Springer-Verlag Berlin Heidelberg
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Weigel, A., Bleisinger, R. (1993). Efficient computation of solutions for contradictory time interval networks. In: Jürgen Ohlbach, H. (eds) GWAI-92: Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0018998
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DOI: https://doi.org/10.1007/BFb0018998
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