Modelling Probabilistic Causation in Decision Making

DI Group Author

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Humans reasoning is based on cause and effect, but these are not enough to draw conclusions due to imperfect information and uncertainty. To solve such problems humans combine causal models and probabilistic information, through probabilistic causation, better known as Causal Bayes Nets. We adopt a logic programming framework and methodology to model our functional description of Causal Bayes Nets, building on its strengths to derive a definition of its semantics. ACORDA is a declarative prospective logic programming which simulates human reasoning in multiple steps into the future. ACORDA is not equipped to deal with probabilistic theory. P-log is a declarative logic programming language used to reason with probabilistic models. Integrated with P-log, ACORDA becomes ready to deal with uncertain problems we face on a daily basis. We show how the integration between ACORDA and P-log was accomplished, and present daily life examples that ACORDA can help people reason about.
Original languageUnknown
Title of host publicationStudies in Computational Intelligence
EditorsK Nakamatsu
Place of PublicationHimeji, Japan
Publication statusPublished - 1 Jan 2009
Event1st KES International Symposium on Intelligent Decision Technologies -
Duration: 1 Jan 2009 → …


Conference1st KES International Symposium on Intelligent Decision Technologies
Period1/01/09 → …

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