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

TL;DR

This paper introduces cDMN, an extension of DMN that integrates constraint reasoning, enabling domain experts to model complex decision logic more effectively while maintaining usability.

Contribution

The paper presents cDMN, a novel extension to DMN that enhances its expressiveness with constraint reasoning, allowing modeling of more complex domain knowledge by non-IT experts.

Findings

cDMN can solve more complex challenges than existing approaches.

cDMN solutions are competitive with top submissions on the DM Community website.

cDMN extends DMN's capabilities without sacrificing user-friendliness.

Abstract

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.