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Min-based causal possibilistic networks: handling interventions and analyzing the possibilistic counterpart of Jeffrey’s rule of conditioning. (English) Zbl 1211.68435

Coelho, Helder (ed.) et al., ECAI 2010. 19th European conference on artificial intelligence, August 16–20, 2010 Lisbon, Portugal. Including proceedings of the 6th prestigious applications of artificial intelligence (PAIS-2010). Amsterdam: IOS Press (ISBN 978-1-60750-605-8/pbk; 978-1-60750-606-5/ebook). Frontiers in Artificial Intelligence and Applications 215, 943-948 (2010).
Summary: This paper deals with two important issues related to the handling of uncertain and causal information in a qualitative (or min-based) possibility theory framework. The first issue addresses encoding interventions using the possibilistic conditioning under uncertain inputs problem. More precisely, we analyze the min-based possibilistic counterpart of Jeffrey’s rule of conditioning and point out that contrary to the probabilistic setting, this rule does not guarantee the existence of a solution satisfying the kinematics conditions. Then we show that this rule can naturally encode the concept of interventions in causal graphical models. Surprisingly enough, we show that when dealing with interventions the min-based counterpart of Jeffrey’s rule provides a unique solution. The second issue deals with the efficient handling of sets of observations and interventions in min-based possibilistic networks, where we propose a solution based on a series of equivalent and efficient transformations on the initial causal graph.
For the entire collection see [Zbl 1207.68003].

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
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