Titlebook: KI 2015: Advances in Artificial Intelligence; 38th Annual German C Steffen H?lldobler,Rafael Pe?aloza,Sebastian Rudol Conference proceeding

Inordinate

Vldl379

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/541644.jpg

APNEA

內(nèi)閣

Complexity of Interval Relaxed Numeric Planning, a problem that is not decidable in general. Relaxation is an approach to simplifying complex problems in order to obtain guidance in the original problem. We present a relaxation approach with intervals for numeric planning and show that plan existence can be decided in polynomial time for tasks w

LAP

Oddness-Based Classifiers for Boolean or?Numerical Datased on the minimization of oddness with respect to the different classes is proposed. Experiments on standard benchmarks with Boolean or numerical data provide good results. The global oddness measure is based on the estimation of the oddness of the new item with respect to the subsets of the classe

conifer

Packing Irregular-Shaped Objects for 3D Printingof triangulated models; the output is a set of location and orientation vectors for the objects. The proposed algorithm consists of three stages: (1) translation of the models into an octree; (2) design of an efficient test for pairwise intersection based on sphere trees; and (3) computation of an o

dandruff

Deciding Subsumers of Least Fixpoint Concepts w.r.t. general ,-TBoxes such subsumptions can be used for computing the logical difference between general .-TBoxes. Our procedure is based on checking for the existence of a certain simulation between hypergraph representations of the set of subsumees of . and of . w.r.t.., respectively. With the aim of keeping the proce

adhesive

Towards a More Efficient Computation of Weighted Conditional Impacts for Relational Probabilistic Knly when moving to the relational case, it has been shown that having the weighted conditional impacts (WCI) of . available, . can be computed much faster. Computing WCI in a straightforward manner readily gets infeasible due to the size of the set . of possible worlds. In this paper, we propose a ne

seroma

GUILT

Short-Term Wind Power Prediction with Combination of Speed and Power Time Seriesavailable, machine learning algorithms can yield very good predictions for short-term forecast horizons. In this paper, we compare the use of wind power and wind speed time series as well as differences of subsequent measurements with Random Forests, Support Vector Regression and .-nearest neighbors