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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 information administration, INAP 2007, and the twenty first Workshop on common sense 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 rigorously reviewed and chosen from a number of submissions. the subjects coated are constraints; databases and knowledge mining; extensions of good judgment programming; and method 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

Sample text

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. The process of searching for such restrictions is called filtering.

Corollary 3. Let a PTSP(T ) be given with T = {t1 , . . , tn } and the objective n r = i=1 αi · si . e. all values of r which are part of any solution of the PTSP(T ) are greater than or equal to lwb”(r) and less than or equal to upb”(r). 6 Empirical Examination For an empirical examination of the influence of the presented bounds during the search for (minimal) solutions of PTSPs we implemented the pruning rules with the presented approximations lwb’ and lwb” as well as their counterparts upb’ and upb” in a specialized WeightedTaskSum constraint in our Java constraint solving library firstCS [6].

Solution time/msec. # choices # backtracks time/msec. # choices # backtracks 8 13 55 25 119 696 674 9 27 91 46 523 2842 2817 10 64 301 181 2212 12242 12177 11 135 623 396 13228 72035 71914 12 153 771 462 51198 302707 302552 Fig. 3. 06 GHz and 1 GB RAM. Linear Weighted-Task-Sum – Scheduling Prioritized Tasks 33 Number of PTSP(T ) with WeightedTaskSum PTSP(T ) with WeightedSum surgeries computation of a 1st opt. solution computation of a 1st opt. solution time/msec. # choices # backtracks time/msec.

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