Modelling probabilistic causation in decision making

Luís Moniz Pereira, Carroline Kencana Ramli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Humans know how to reason based on cause and effect, but cause and effect is not enough to draw conclusions due to the problem of imperfect information and uncertainty. To resolve these problems, humans reason combining causal models with probabilistic information. The theory that attempts to model both causality and probability is called probabilistic causation, better known as Causal Bayes Nets. In this work we henceforth adopt a logic programming framework and methodology to model our functional description of Causal Bayes Nets, building on its many strengths and advantages to derive a consistent definition of its semantics. ACORDA is a declarative prospective logic programming which simulates human reasoning in multiple steps into the future. ACORDA itself is not equipped to deal with probabilistic theory. On the other hand, P-log is a declarative logic programming language that can be used to reason with probabilistic models. Integrated with P-log, ACORDA becomes ready to deal with uncertain problems that we face on a daily basis. We show how the integration between ACORDA and P-log has been accomplished, and we present cases of daily life examples that ACORDA can help people to reason about.

Original languageEnglish
Title of host publicationNew Advances in Intelligent Decision Technologies
Subtitle of host publicationResults of the First KES International Symposium IDT 2009
EditorsKazumi Nakamatsu, Gloria Phillips-Wren, Lakhmi C. Jain, Robert J. Howlett
Place of PublicationBerlin
PublisherSpringer
Pages85-95
Number of pages11
ISBN (Electronic)978-3-642-00909-9
ISBN (Print)978-3-642-00908-2, 978-3-642-10160-1
DOIs
Publication statusPublished - 2009
Event1st KES International Symposium on Intelligent Decision Technologies -
Duration: 1 Jan 2009 → …

Publication series

NameStudies in Computational Intelligence
Volume199
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

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

Keywords

  • Logic Programming
  • Integrity Constraint
  • Preference Rule
  • Probabilistic Causation
  • Risky Event

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