Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Mind and Mechanism
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Revised stable models – a semantics for logic programs
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
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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 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.