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CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
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@MISC{Vennekens_cp-logic:a,
author = {Joost Vennekens and Marc Denecker and Maurice Bruynooghe},
title = {CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming },
year = {}
}
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Abstract
We examine the relation between constructive processes and the concept of causality. We observe that causality has an inherent dynamic aspect, i.e., that, in essence, causal information concerns the evolution of a domain over time. Motivated by this observation, we construct a new representation language for causal knowledge, whose semantics is defined explicitly in terms of constructive processes. This is done in a probabilistic context, where the basic steps that make up the process are allowed to have non-deterministic effects. We then show that a theory in this language defines a unique probability distribution over the possible outcomes of such a process. This result offers an appealing explanation for the usefulness of causal information and links our explicitly dynamic approach to more static causal probabilistic modeling languages, such as Bayesian networks. We also show that this language, which we have constructed to be a natural formalization of a certain kind of causal statements, is closely related to logic programming. This result demonstrates that, under an appropriate formal semantics, a rule of a normal, a disjunctive or a certain kind of probabilistic logic program can be interpreted as a description of a causal event.
Keyphrases
causal probabilistic event logic programming constructive process certain kind causal information bayesian network causal knowledge probabilistic context natural formalization static causal probabilistic modeling language dynamic approach new representation language inherent dynamic aspect probabilistic logic program non-deterministic effect causal event causal statement possible outcome appropriate formal semantics basic step unique probability distribution
