# Direct Encoding of Declare Constraints in ASP

arXiv: https://arxiv.org/abs/2412.10152 · HTML: https://mlsanity.com/paper/2412.10152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Conformance Checking Based on Multi-Perspective Declarative Process Models](https://mlsanity.com/paper/1503.04957/) (2015)
- [Little Tricky Logic: Misconceptions in the Understanding of LTL](https://mlsanity.com/paper/2211.01677/) (2022)
- [Process discovery on deviant traces and other stranger things](https://mlsanity.com/paper/2109.14883/) (2021)

## Abstract (arXiv)

Answer Set Programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a means to model processes through sets of constraints valid traces must satisfy, that can be expressed in Linear Temporal Logic over Finite Traces (LTLf). Existing ASP-based solutions encode Declare constraints by modeling the corresponding LTLf formula or its equivalent automaton which can be obtained using established techniques. In this paper, we introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules, eliminating the need for intermediate representations. We assess the effectiveness of this novel approach on two Process Mining tasks by comparing it with alternative ASP encodings and a Python library for Declare. Under consideration in Theory and Practice of Logic Programming (TPLP).