  Automated commonsense reasoning is essential for building human-like AI
systems featuring, for example, explainable AI. Event Calculus (EC) is a family
of formalisms that model commonsense reasoning with a sound, logical basis.
Previous attempts to mechanize reasoning using EC faced difficulties in the
treatment of the continuous change in dense domains (e.g., time and other
physical quantities), constraints among variables, default negation, and the
uniform application of different inference methods, among others. We propose
the use of s(CASP), a query-driven, top-down execution model for Predicate
Answer Set Programming with Constraints, to model and reason using EC. We show
how EC scenarios can be naturally and directly encoded in s(CASP) and how it
enables deductive and abductive reasoning tasks in domains featuring
constraints involving both dense time and dense fluents.
