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

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...

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Published inTheory and practice of logic programming Vol. 23; no. 3; pp. 535 - 558
Main Authors VANDEVELDE, SIMON, AERTS, BRAM, VENNEKENS, JOOST
Format Journal Article
LanguageEnglish
Published Cambridge Cambridge University Press 01.05.2023
Subjects
Community
Computer science
Constraint modelling
Engineers
Knowledge
Museums
Optimization
Readability
Semantics
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Summary: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.
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ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068421000491