Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning
This work addresses the problem of knowledge-based AI accessibility for domain experts, though it appears incremental as an extension of an existing standard.
The paper tackles the challenge of enabling domain experts to model complex domain knowledge without IT assistance by extending the Decision Model and Notation (DMN) standard to Constraint Decision Model and Notation (cDMN), and finds that cDMN is competitive with existing solutions and solves more challenges than any other approach.
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.