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Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

Published online by Cambridge University Press:  12 November 2021

SIMON VANDEVELDE Open the ORCID record for SIMON VANDEVELDE [Opens in a new window] ,
BRAM AERTS  and
JOOST VENNEKENS
SIMON VANDEVELDE
Affiliation:
KU Leuven, De Nayer Campus, Department of Computer Science J.-P. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium Leuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium (e-mails: [email protected], [email protected], [email protected])
BRAM AERTS
Affiliation:
KU Leuven, De Nayer Campus, Department of Computer Science J.-P. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium Leuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium (e-mails: [email protected], [email protected], [email protected])
JOOST VENNEKENS
Affiliation:
KU Leuven, De Nayer Campus, Department of Computer Science J.-P. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium Leuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium (e-mails: [email protected], [email protected], [email protected])
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Abstract

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Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.

Keywords

decision model and notationconstraint reasoningexpressivenessreadabilityIDP system
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 3: Special Issue on Logic Rules and Reasoning: Selected Papers from the 4th International Joint Conference on Rules and Reasoning (RuleML+RR 2020) , May 2023 , pp. 535 - 558
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

This research received funding from the Flemish Government under the Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen programme.

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