Abstract
Answer Set Programming (ASP), a well-known declarative programming paradigm, has recently found practical application in Process Mining, particularly in tasks involving declarative specifications of business processes. Declare is the most popular declarative process modeling language. It provides a way to model processes by sets of constraints, expressed in Linear Temporal Logic over Finite Traces (LTL\(_\text {f}\)), that valid traces must satisfy. Existing ASP-based solutions encode a Declare constraint by the corresponding LTL\(_\text {f}\) formula or its equivalent automaton, derived using well-established techniques. In this paper, we propose a novel encoding for Declare constraints, which models their semantics directly as ASP rules, without resorting to intermediate representations. We evaluate the effectiveness of the novel approach on two Process Mining tasks by comparing it to alternative ASP encodings and a Python library for Declare .
This work was partially supported by the Italian Ministry of Research (MUR) under PRIN project PINPOINT - CUP H23C22000280006, PRIN project HypeKG - CUP H53D23003710006, PRIN PNRR project DISTORT - CUP H53D23008170001, PNRR projects FAIR “Future AI Research” - Spoke 9 - WP9.1 and WP9.2- CUP H23C22000860006 and by the project “Borgo 4.0”, POR Campania FESR 2014–2020.
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Chiariello, F., Fionda, V., Ielo, A., Ricca, F. (2023). A Direct ASP Encoding for Declare. In: Gebser, M., Sergey, I. (eds) Practical Aspects of Declarative Languages. PADL 2024. Lecture Notes in Computer Science, vol 14512. Springer, Cham. https://doi.org/10.1007/978-3-031-52038-9_8
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