×

zbMATH — the first resource for mathematics

Possibilistic graphical models. (English) Zbl 0979.68106
Della Riccia, Giacomo (ed.) et al., Computational intelligence in data mining. 3rd workshop, Udine, Italy, October 8-10, 1998. Wien: Springer. CISM Courses Lect. 408, 51-67 (2000).
Summary: Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems. Its most prominent representatives are Bayesian networks and Markov networks for probabilistic reasoning, which have been well-known for over ten years now. However, they suffer from certain deficiencies, if imprecise information has to be taken into account. Therefore possibilistic graphical modeling has recently emerged as a promising new area of research. Possibilistic networks are a noteworthy alternative to probabilistic networks whenever it is necessary to model both uncertainty and imprecision. Imprecision, understood as set-valued data, has often to be considered in situations in which information is obtained from human observers or imprecise measuring instruments. In this paper we provide an overview on the state of the art of possibilistic networks w.r.t. to propagation and learning algorithms.
For the entire collection see [Zbl 0962.00006].

MSC:
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
PDF BibTeX XML Cite