@misc{Gebhardt_Jörg_Automated,
 author={Gebhardt, Jörg and Kruse, Rudolf},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={Possibilistic networks constitute a promising framework for efficient treatment of uncertain and imprecise information in knowledge-based systems. In this paper, we propose a new method for induction of the structure (the qualitative part) and the attached possibility distributions (the quantitative part) of a possibilistic network from a database of sample cases that may contain imprecise or missing values.},
 abstract={It turns out that a modified random-set approach to the semantics of possibility distributions is adequate to provide a possibilistic interpretation of the databases under consideration. Since constructing a possibilistic network can be viewed as a generalization of the structure identification problem in relational data, we have to overcome well-known complexity problems.},
 abstract={Therefore we present an efficient Greedy search structure induction algorithm for possibilistic networks that has successfully been applied to construct a non-trivial network of practical interest from a given database.},
 type={artykuł},
 title={Automated construction of possibilistic networks from data},
 keywords={sterowanie, sterowanie-teoria, sztuczna inteligencja, matematyka stosowana, informatyka},
}