mlsanityData to 7 Oct 2026

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

arXiv 2110.02610 ↗First posted 6 Oct 2021cs.AIcited by 3

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Abstract (arXiv)

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 DMN's 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 17 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

3 of 17 references.

References · 3

  1. 2014Predicate Logic as a Modelling Language: The IDP Systemcited by 8
  2. 2020Tackling the DMN Challenges with cDMN: a Tight Integration of DMN and constraint reasoningcited by 3
  3. 2018Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledgecited by 2

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