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Automated commonsense reasoning is essential for building human-like AIsystems featuring, for example, explainable AI. Event Calculus (EC) is a familyof formalisms that model commonsense reasoning with a sound, logical basis.Previous attempts to mechanize reasoning using EC faced difficulties in thetreatment of the continuous change in dense domains (e.g., time and otherphysical quantities), constraints among variables, default negation, and theuniform application of different inference methods, among others. We proposethe use of s(CASP), a query-driven, top-down execution model for PredicateAnswer Set Programming with Constraints, to model and reason using EC. We showhow EC scenarios can be naturally and directly encoded in s(CASP) and how itenables deductive and abductive reasoning tasks in domains featuringconstraints involving both dense time and dense fluents.

Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming