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

Proceedings of the Annual Hawaii International Conference on System Sciences

Callewaert²,

Vennekens³

2022

Self Cite

The Decision Model and Notation (DMN) standard is a user-friendly notation for decision logic. To verify correctness of DMN decision tables, many tools are available. However, most of these look at a table in isolation, with little or no regards for its context. In this work, we argue for the importance of context, and extend the formal verification criteria to include it. We identify two forms of context, namely in-model context and background knowledge. We also present our own context-aware verification tool, implemented in our DMN-IDP interface, and show that this context-aware approach allows us to perform more thorough verification than any other available tool.

Section: Methodsmentioning

confidence: 99%

“…It was designed by the Object Management Group (OMG), who aimed at creating a notation that is readable, user-friendly and executable. Because of these features, the standard has quickly gained popularity in both industry [2,3,4] and academia [5,6].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Verification of DMN

Proceedings of the Annual Hawaii International Conference on System Sciences

Callewaert²,

Vennekens³

2022

Self Cite

“…These glossary tables contain the required meta-information to correctly interpret the pDMN model. This is analogous to the approach used in cDMN (Aerts, Vandevelde, and Vennekens 2020).…”

Section: Glossarymentioning

confidence: 97%

“…work on cDMN (Aerts, Vandevelde, and Vennekens 2020), that extended DMN with constraint programming.…”

Section: Introductionmentioning

confidence: 99%

A Table-Based Representation for Probabilistic Logic: Preliminary Results

Verreet²,

Raedt³

et al. 2021

Preprint

Self Cite

We present Probabilistic Decision Model and Notation (pDMN), a probabilistic extension of Decision Model and Notation (DMN). DMN is a modeling notation for deterministic decision logic, which intends to be user-friendly and low in complexity. pDMN extends DMN with probabilistic reasoning, predicates, functions, quantification, and a new hit policy. At the same time, it aims to retain DMN's userfriendliness to allow its usage by domain experts without the help of IT staff. pDMN models can be unambigiously translated into ProbLog programs to answer user queries. ProbLog is a probabilistic extension of Prolog flexible enough to model and reason over any pDMN model.

“…This paper is an extended version of a paper we presented at the RuleML+RR 2020 conference (Aerts et al 2020). It includes an updated list of challenges, changes to the semantics to make it more complete, a more in-depth description of the solver and a section on the integration of DMN into business models.…”

mentioning

confidence: 99%

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

Theory and Practice of Logic Programming

Aerts²,

Vennekens³

2021

Self Cite

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.