Download Applications of Declarative Programming and Knowledge by Ulrich Geske, Hans-Joachim Goltz (auth.), Dietmar Seipel, PDF

By Ulrich Geske, Hans-Joachim Goltz (auth.), Dietmar Seipel, Michael Hanus, Armin Wolf (eds.)

This quantity constitutes the completely refereed post-conference complaints of the seventeenth foreign convention on functions of Declarative Programming and data administration, INAP 2007, and the twenty first Workshop on good judgment Programming, WLP 2007, held in Würzburg, Germany, in the course of October 4-6, 2007.

The sixteen completely revised complete papers provided including 1 invited paper have been rigorously reviewed and chosen from a variety of submissions. the themes coated are constraints; databases and information mining; extensions of common sense programming; and procedure demonstrations.

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Extra info for Applications of Declarative Programming and Knowledge Management: 17th International Conference, INAP 2007, and 21st Workshop on Logic Programming, WLP 2007, Würzburg, Germany, October 4-6, 2007, Revised Selected Papers

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2 3 The maximization of the sum is a symmetric problem and handled analogously. The inequalities reflect the fact that breaks between tasks are allowed. 26 4 A. Wolf and G. Schrader Better Pruning for the PTSP In the following we consider a weighted sum constraint r = ni=1 αi · si with positive rational factors α1 , . . , αn and finite domain variables s1 ∈ S1 , . . , sn ∈ Sn and r ∈ R. In detail, the domains of these variables are finite sets of integer values. A pruning scheme for such a weighted sum has been proposed in [8].

3010, pp. 223–232. : Better propagation for non-preemptive single-resource constraint problems. , Rossi, F. ) CSCLP 2004. LNCS, vol. 3419, pp. 201–215. de Abstract. Unary resources play a central role in modelling scheduling problems. Edge-finding is one of the most popular techniques to deal with unary resources in constraint programming environments. Often it depends on external factors if an activity will be included in the final schedule, making the activity optional. Currently known edge-finding algorithms cannot take optional activities into account.

Let a PTSP(T ) with ordered tasks T = {t1 , . . , tn } be given such that αdii ≤ dj αj holds for 1 ≤ i < j ≤ n. Then, especially during the search for (minimal) solutions of the PTSP some of the tasks will be scheduled. Thus, there will be a partition of already scheduled tasks S = {tp1 , . . , tpq } and unscheduled tasks U = {tu1 , . . , tuv } such that S ∪ U = T and S ∩ U = ∅ holds. e. Sp1 = {w1 }, . . , Spq = {wq }. e. |Su1 | > 1, . . , |Suv | > 1. Following Corollary 1 for all potential start times s1 ∈ S1 , .

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