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

Published online by Cambridge University Press:  02 November 2021

JOAQUÍN ARIAS
Affiliation:
CETINIA, Universidad Rey Juan Carlos, Madrid, Spain (e-mail: [email protected])
MANUEL CARRO
Affiliation:
IMDEA Software Institute, Madrid, Spain Universidad Politécnica de Madrid, Madrid, Spain (e-mails: [email protected], [email protected])
ZHUO CHEN
Affiliation:
University of Texas at Dallas, Richardson, USA (e-mail: [email protected])
GOPAL GUPTA
Affiliation:
University of Texas at Dallas, Richardson, USA (e-mail: [email protected])

Abstract

Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR 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.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

This paper is an extended version of the work by Arias et al. (2019)

Work partially supported by EIT Digital, MICINN projects RTI2018-095390-B-C33 InEDGEMobility (MCIU/AEI/FEDER, UE), PID2019-108528RB-C21 ProCode, Comunidad de Madrid project S2018/TCS-4339 BLOQUES-CM co-funded by EIE Funds of the European Union, US NSF Grants IIS 1718945, IIS 1910131, IIP 1916206.

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