Title: Representing causal information about a probabilistic process
Authors: Vennekens, Joost ×
Denecker, Marc
Bruynooghe, Maurice #
Issue Date: 2006
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:4160 pages:452-464
Conference: European Conference on Logics in Artificial Intelligence (JELIA) edition:10 location:Liverpool, United Kingdom date:13-15 September 2006
Abstract: We study causal information about probabilistic processes, i.e., information about why events occur. A language is developed in which such information can be formally represented and we investigate when this suffices to uniquely characterize the probability distribution that results from such a process. We examine both detailed representations of temporal aspects and representations in which time is implicit. In this last case, our logic turns into a more fine-grained version of Pearl's approach to causality. We relate our logic to certain probabilistic logic programming languages, which leads to a clearer view on the knowledge representation properties of these language. We show that our logic induces a semantics for disjunctive logic programs, in which these represent non-deterministic processes. We show that logic programs under the well-founded semantics can be seen as a language of deterministic causality, which we relate to McCain & Turner's causal theories.
Description: acceptance rate=44%
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
Technologiecluster Computerwetenschappen
Computer Science Technology TC, Technology Campus De Nayer Sint-Katelijne-Waver
× corresponding author
# (joint) last author

Files in This Item:
File Status SizeFormat
42258.pdf Published 191KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science