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

TL;DR

This paper introduces a novel approach for modeling and reasoning in Event Calculus using goal-directed Constraint Answer Set Programming, enabling effective handling of dense temporal and physical constraints for commonsense reasoning.

Contribution

It demonstrates how s(CASP), a query-driven ASP with constraints, can be used to directly encode and reason with Event Calculus in dense domains, improving reasoning capabilities.

Findings

Effective encoding of EC scenarios in s(CASP)

Supports deductive and abductive reasoning with dense constraints

Addresses previous limitations in continuous change modeling

Abstract

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.