Titlebook: KI 2016: Advances in Artificial Intelligence; 39th Annual German C Gerhard Friedrich,Malte Helmert,Franz Wotawa Conference proceedings 2016

節(jié)約

Mona Lange,Felix Kuhr,Ralf M?llerternen Herausforderungen, mit denen man konfrontiert ist, kaum beigetragen hat. Das Verh?ltnis von Aufwand und Ergebnis, so scheint es, f?llt mehr als kümmerlich aus. Wahlmüdigkeit, ja eine unverkennbare Politikverdrossenheit sind die Folgen. Eines der Hauptprobleme, die viel beklagte Kluft zwischen

思想流動(dòng)

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/541645.jpg

格言

https://doi.org/10.1007/978-3-319-46073-4human-robot interaction; linear programming; information retrival; machine learning; social robotics; ans

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978-3-319-46072-7Springer International Publishing AG 2016

Tracheotomy

KI 2016: Advances in Artificial Intelligence978-3-319-46073-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

Conflagration

Using Ontological Knowledge About Active Pharmaceutical Ingredients for a Decision Support System inng the resulting complexity must be able to reason about the respective active ingredients and their interrelationships. In this paper, we present a corresponding ontology and illustrate its use for answering queries relevant for clinical therapy decisions.

Senescent

Conference proceedings 2016rreichische Gesellschaft für Artificial Intelligence, ?GAI, held in Klagenfurt, Austria, in September 2016..The 8 revised full technical papers presented together with 12 technical communications, and 16 extended abstracts were carefully reviewed and selected from 44 submissions..The conference prov

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0302-9743 h the ?sterreichische Gesellschaft für Artificial Intelligence, ?GAI, held in Klagenfurt, Austria, in September 2016..The 8 revised full technical papers presented together with 12 technical communications, and 16 extended abstracts were carefully reviewed and selected from 44 submissions..The confe

Infect

Lifted Junction Tree Algorithmat compactly represent symmetries. First experiments show that we speed up the computation time compared to the propositional version. When querying for multiple marginals, the lifted junction tree algorithm performs better than using lifted VE to infer each marginal individually.

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