Titlebook: KI 2015: Advances in Artificial Intelligence; 38th Annual German C Steffen Hölldobler,Rafael Peñaloza,Sebastian Rudol Conference proceeding

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书目名称KI 2015: Advances in Artificial Intelligence影响因子(影响力)




书目名称KI 2015: Advances in Artificial Intelligence影响因子(影响力)学科排名




书目名称KI 2015: Advances in Artificial Intelligence网络公开度




书目名称KI 2015: Advances in Artificial Intelligence网络公开度学科排名




书目名称KI 2015: Advances in Artificial Intelligence被引频次




书目名称KI 2015: Advances in Artificial Intelligence被引频次学科排名




书目名称KI 2015: Advances in Artificial Intelligence年度引用




书目名称KI 2015: Advances in Artificial Intelligence年度引用学科排名




书目名称KI 2015: Advances in Artificial Intelligence读者反馈




书目名称KI 2015: Advances in Artificial Intelligence读者反馈学科排名

Judicious

A Serendipity Model for News Recommendationof serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study.

Asymptomatic

ABHOR

0302-9743 nity to present a wider range of results and ideas that are of interest to the KI audience, including reports about recent own publications, position papers, and previews of ongoing work..978-3-319-24488-4978-3-319-24489-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

值得

0302-9743 ence, KI 2015, held in Dresden, Germany, in September 2015. ..The 15 revised full technical papers presented together with 14 technical communications, 4 doctoral consortium contributions,  and 3 keynotes were carefully reviewed and selected from 58 submissions. ..The conference provides the opportu

Generosity

Kernel Feature Maps from Arbitrary Distance Metricsg pseudo line projections called . (DBFM). We show that our approximation does not depend on the input dataset size or the dimension of the input space. We experimentally evaluate our approach on two real datasets using two metric and one non-metric distance function.

engrave

Necessary Observations in Nondeterministic Planningcally, we develop a bottom-up algorithm which computes a set of variables which are always necessary to observe, in order to always reach a goal state. Our experimental results show that the knowledge about necessary observation variables can be used to minimize the number of sensors of an agent.

单片眼镜

Fatten

Substance

Complexity of Interval Relaxed Numeric Planningoblem. We present a relaxation approach with intervals for numeric planning and show that plan existence can be decided in polynomial time for tasks where dependencies between numeric effects are acyclic.