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 its expressiveness makes it possible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving both dense time and dense fluents.
Work partially supported by EIT Digital, MINECO project TIN2015-67522-C3-1-R (TRACES), Comunidad de Madrid project S2018/TCS-4339 BLOQUES-CM co-funded by EIE Funds of the European Union, and US NSF Grant IIS 1718945.
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Notes
- 1.
For implementation convenience, and without loss of expressiveness, we assume that argument \(t_2\) in \(Trajectory(f_1, t_1, f_2, t_2)\) is not a time difference w.r.t. \(t_1\), but an absolute time after \(t_1\).
- 2.
For simplicity the amount of water filled/leaked correspond directly to how long the water has been pouring in/spilling from the vessel.
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Appendices
A F2LP Encoding of the Light Scenario
The next figure shows the F2LP [15] program for the light scenario described in Sect. 5 using discrete Event Calculus. Since the directive is not available in clingo 5.1.1 [7], we had to adapt the translation of F2LP adding and to make the clauses safe.
B Adapted F2LP Translation of the Light Scenario with Increased Precision
The next figure shows an F2LP [15] program for the light scenario described in Sect. 5, where the new predicate makes it possible to have a finer grain for the possible values of by increasing the value of .
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Arias, J., Chen, Z., Carro, M., Gupta, G. (2020). Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming. In: Gabbrielli, M. (eds) Logic-Based Program Synthesis and Transformation. LOPSTR 2019. Lecture Notes in Computer Science(), vol 12042. Springer, Cham. https://doi.org/10.1007/978-3-030-45260-5_9
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