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 lawsuits of the seventeenth overseas convention on purposes of Declarative Programming and information 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 awarded including 1 invited paper have been conscientiously reviewed and chosen from a variety of submissions. the subjects coated are constraints; databases and knowledge mining; extensions of common sense programming; and process demonstrations.

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Additional resources 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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See Fig. 2 for a sample instance. When the set T of activities grows, the number of possibilities to order them grows exponentially; it is long known that the unary resource scheduling problem is NP-complete [4, p. 236]. In constraint programming, the number of considered activity orderings is reduced in the following way: For each possible ordering of activities i and j (i before j or j before i), restrictions for the placement of other activities are derived before any other ordering decision is made.

Wolf and G. Schrader d Corollary 2. Let a PTSP(T ) be given with T = {t1 , . . , tn } such that αdii ≥ αjj holds for 1 ≤ i < j ≤ n. Further, let en = max(S1 ∪ . . ∪ Sn ), the objective n r = i=1 αi · si , and ⎢ ⎥ ⎢ n ⎥ n−1 ⎢ ⎥ ⎣ upb’(r) = αi · (en − dj )⎦ i=1 j=i be defined. e. all values of r which are part of any solution of the PTSP(T ) are especially less than or equal to upb’(r). 5 Practical Application For a practical application of Theorem 1 and its corollaries in a constraint programming system any change of the potential start times of the tasks has to be considered immediately.

The used branching scheme is a left-to-right, depth-first incremental tree search which avoids re-traversals of already visited paths in the search tree containing suboptimal solutions (cf. [9]). The applied search strategy is specialized for contiguous task scheduling [14]. The pruning rules implemented in the WeightedTaskSum constraint are applied to real life task scheduling problems like the scheduling of surgeries in hospitals. The latter was indeed the ultimate cause to search for better and specialized pruning algorithms: the respond times of an interactive surgery scheduler based on our Java constraint solving library firstCS [6] were not satisfactory.

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