Modelling decision making with probabilistic causation

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 system 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 ...

• Type: Journal
• Year: 2010
• Where: IDT
• Authors: Luís Moniz Pereira, Carroline Kencana Ramli
• Keywords: Causal Bayes Nets, IDT 2010, Logic Programming, Probabilistic