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

arXiv: https://arxiv.org/abs/2110.02610 · HTML: https://mlsanity.com/paper/2110.02610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Predicate Logic as a Modelling Language: The IDP System](https://mlsanity.com/paper/1401.6312/) (2014)
- [Tackling the DMN Challenges with cDMN: a Tight Integration of DMN and constraint reasoning](https://mlsanity.com/paper/2005.09998/) (2020)
- [Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge](https://mlsanity.com/paper/1807.11615/) (2018)

## Built on by (most cited first, 3 of 3)

- [Interactive Model Expansion in an Observable Environment](https://mlsanity.com/paper/2305.17140/) (2023)
- [Knowledge-Based Support for Adhesive Selection: Will it Stick?](https://mlsanity.com/paper/2311.06302/) (2023)
- [An epistemic logic for modeling decisions in the context of incomplete knowledge](https://mlsanity.com/paper/2312.11186/) (2023)

## 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.