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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: s.vandevelde@kuleuven.be, b.aerts@kuleuven.be, joost.vennekens@kuleuven.be)
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: s.vandevelde@kuleuven.be, b.aerts@kuleuven.be, joost.vennekens@kuleuven.be)
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: s.vandevelde@kuleuven.be, b.aerts@kuleuven.be, joost.vennekens@kuleuven.be)
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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.

References

Aerts, B., Vandevelde, S. and Vennekens, J. 2020. Tackling the DMN challenges with cDMN: A tight integration of dmn and constraint reasoning. In Rules and Reasoning: Fourth International Joint Conference, RuleML+RR 2020, Oslo, Norway, June 29 - July 1, 2020, Proceedings. Proceedings of RuleML+RR 2020, 23–38.Google Scholar
Bazhenova, E., Zerbato, F., Oliboni, B. and Weske, M. 2019. From BPMN process models to DMN decision models. Information Systems 83, 69–88.CrossRefGoogle Scholar
Biard, T., Le Mauff, A., Bigand, M. and Bourey, J.-P. 2015. Separation of decision modeling from business process modeling using new “Decision Model and Notation” (DMN) for automating operational decision-making. In Risks and Resilience of Collaborative Networks, L. M. Camarinha-Matos, F. Bénaben, and W. Picard, Eds. Springer International Publishing, Cham, 489–496.Google Scholar
Bruynooghe, M., BLOCKEEL, H., BOGAERTS, B., DE CAT, B., DE POOTER, S., JANSEN, J., LABARRE, A., RAMON, J., DENECKER, M. and VERWER, S. 2015. Predicate logic as a modeling language: Modeling and solving some machine learning and data mining problems with IDP3. Theory and Practice of Logic Programming 15, 783–817.Google Scholar
Calvanese, D., Dumas, M., Laurson, U., Maggi, F. M., Montali, M. and Teinemaa, I. 2018. Semantics, analysis and simplification of DMN decision tables. Information Systems (Oxford) 78, 112–125.CrossRefGoogle Scholar
Calvanese, D., Montali, M., Dumas, M. and Maggi, F. 2019. Semantic DMN: Formalizing and reasoning about decisions in the presence of background knowledge. Theory and Practice of Logic Programming 19, 4, 536–573.CrossRefGoogle Scholar
Car, N. J. 2018. Using decision models to enable better irrigation decision support systems. Computers and Electronics in Agriculture 152, 290–301.CrossRefGoogle Scholar
Carbonnelle, P., Aerts, B., Deryck, M., Vennekens, J. and Denecker, M. 2019. An interactive consultant. In Proceedings of the 31st Benelux Conference on Artificial Intelligence, K. Beuls, B. Bogaerts, G. Bontempi, P. Geurts, N. Harley, B. Lebichot, T. Lenaerts, G. Louppe, and P. V. Eecke, Eds. CEUR Workshop Proceedings, vol. 2491. CEUR-WS.org.Google Scholar
De Cat, B., Bogaerts, B., Bruynooghe, M., Janssens, G. and Denecker, M. 2018. Predicate logic as a modeling language: The IDP system. In Declarative Logic Programming: Theory, Systems, and Applications. ACM Books, 279–329.Google Scholar
Deryck, M., Aerts, B. and Vennekens, J. 2019. Adding constraint tables to the DMN standard: Preliminary results. In Rules and Reasoning: Third International Joint Conference, RuleML+RR 2019, Bolzano, Italy, September 16–19, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11784, 171–179.Google Scholar
Hasic, F., De Smedt, J. and Vanthienen, J. 2017. Towards assessing the theoretical complexity of the decision model and notation (DMN) research-in-progress. In CEUR Workshop Proceedings. Vol. 1859. CEUR Workshop Proceedings, 64–71. ISSN: 1613-0073.Google Scholar
Hasic, F. and Vanthienen, J. 2020. From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in belgium. Knowledge and Information Systems 62, 5, 2011–2028.CrossRefGoogle Scholar
Hasi, F., De Smedt, J. and Vanthienen, J. 2018. Augmenting processes with decision intelligence: Principles for integrated modelling. Decision Support Systems 107, 1–12.Google Scholar
Object Management Group. 2020. Decision Model and Notation.Google Scholar
OpenRules, Inc. 2017. Openrules.Google Scholar
Progress. 2019. Corticon.Google Scholar
Silver, B. 2018. DMN Method and Style: Business Practitioner’s Guide to Decision Modeling, 2nd ed. ed. Cody-Cassidy Press, Altadena.Google Scholar
Sooter, L. J., Hasley, S., Lario, R., Rubin, K. S. and Hasić, F. 2019. Modeling a clinical pathway for contraception. Applied Clinical Informatics 10, 5 (October), 935–943.Google Scholar
Vandevelde, S. and Vennekens, J. 2020. A Multifunctional, Interactive DMN Decision Modelling Tool.Google Scholar
Wittocx, J., Mariën, M. and Denecker, M. 2008. The IDP system: A model expansion system for an extension of classical logic. Proceedings of the 2nd Workshop on Logic and Search, 153–165.Google Scholar