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

arXiv: https://arxiv.org/abs/2106.14566 · HTML: https://mlsanity.com/paper/2106.14566/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [Computing Stable Models of Normal Logic Programs Without Grounding](https://mlsanity.com/paper/1709.00501/) (2017)
- [Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming](https://mlsanity.com/paper/1401.4607/) (2014)
- [PDDL+ Planning via Constraint Answer Set Programming](https://mlsanity.com/paper/1609.00030/) (2016)
- [Description, Implementation, and Evaluation of a Generic Design for Tabled CLP](https://mlsanity.com/paper/1809.05771/) (2018)

## Built on by (most cited first, 4 of 4)

- [Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming](https://mlsanity.com/paper/2109.04634/) (2021)
- [Towards Dynamic Consistency Checking in Goal-directed Predicate Answer Set Programming](https://mlsanity.com/paper/2110.12053/) (2021)
- [Locksynth: Deriving Synchronization Code for Concurrent Data Structures with ASP](https://mlsanity.com/paper/2305.18225/) (2023)
- [Counterfactual Explanation Generation with s(CASP)](https://mlsanity.com/paper/2310.14497/) (2023)

## Abstract (arXiv)

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.