In this paper we present two approaches, namely correlative and static causality, to study cause-effect relationships in reaction models and we propose a framework which integrates them in order to study causality by means of transition P systems. The proposed framework is based on the fact that statistical analysis can be used to building up a membrane model which can be used to analyze causality relationships in terms of multisets of objects and rules in presence of non-determinism and parallelism. We prove that the P system which is defined by means of correlation analysis provides a correspondence between the static and correlative notions of causality. © 2013 Springer-Verlag.
CITATION STYLE
Pagliarini, R., Agrigoroaiei, O., Ciobanu, G., & Manca, V. (2012). An analysis of correlative and static causality in P systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7762 LNCS, pp. 323–341). https://doi.org/10.1007/978-3-642-36751-9_22
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