C-Log: A Knowledge Representation Language of Causality

ABSTRACT. Cause-effect relations are an important part of human knowledge. In real life, humans often reason about complex causes linked to complex effects. By comparison, existing formalisms for representing knowledge about causal relations are quite limited in the kind of specifications of causes and effects they allow. In this paper, we present the new language C-Log, which offers a significantly more expressive representation of effects. We show how Approximation Fixpoint Theory can be used to define a formal semantics that captures the intuitions underlying this language. We also compare C-Log with several related languages and paradigms, including inductive definitions, disjunctive logic programming, and extensions of Datalog and we show that we provided a new semantics for (a generalisation of) disjunctive logic programming with existential quantifications in rule heads.