A Multifunctional, Interactive
DMN Decision Modelling Tool
Simon Vandevelde?and Joost Vennekens
KU Leuven, De Nayer Campus, Dept. of Computer Science
Leuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium
Abstract. In this demo we showcase DMN-IDP, a user-friendly tool
which combines the readability of the Decision Model and Notation
(DMN) standard with the power of the IDP system through an interac-
The Decision Model and Notation (DMN) standard is a table-based way
of representing decision logic, with a focus on readability and user-friendliness.
Designed by the Object Management Group, it was quickly adopted in various
industries. In academia, interest in DMN to represent knowledge is also growing,
because of its accessibility as a modelling language for domain experts . To use
DMN models, tools exist which can compute a suitable assignment of values to
the decision variables, given the values of the environmental variables, by means
of forward propagation.
In , it was argued that the knowledge expressed in a DMN model can be
used for much more. For instance, value propagation can also be done in other
directions, such as from decision to environmental variables. Other examples
are reasoning on incomplete data, and applying diﬀerent inference tasks, such
as optimization. To illustrate their approach, the authors made use of the IDP
knowledge base system . By manually translating DMN models into ﬁrst-order
logic knowledge bases (KBs), users could interact with the KB in a user-friendly
way via a browser-based interface. While this results in a powerful and ﬂexible
way of working, there are two main downsides. Firstly, the DMN models need to
be created in a separate tool. Secondly, the translation from DMN to IDP KB is
done manually, for which knowledge of the representation language of the IDP
system is required.
In this demo, we present DMN-IDP, a full-ﬂedged DMN tool which combines
the dmn-js DMN editor  and the IDP-based Interactive Consultant interface
. Using this tool, a user can upload or create DMN models, which are then
translated into IDP KBs. Users can interact with these models via the Inter-
active Consultant interface. The translation from DMN to IDP is done by the
same transformation used in the cDMN framework . The interface supports
propagating values in any direction, reasoning on incomplete data, optimization
of values and explanation of decisions. In this way, DMN models become useable
in more situations, removing the need to build speciﬁc models for every target
output in a use case.
?This research received funding from the Flemish Government under the “Onderzoek-
sprogramma Artiﬁci¨ele Intelligentie (AI) Vlaanderen” programme.
Calculate Body Mass Index
UWeight(kg) Height(m) BMI
1 — — weight / (height * height)
Decide BMI Level
UBMI BMI Level Risk Level
2[18.5..24.8] Normal Low
3[25..29.9] Overweight Increased
4[30..34.9] Obese I High
5[35..39.9] Obese II Very High
6>39.9Extreme Obesity Extremely High
Fig. 1: A DMN model for deciding a patient’s BMI level and risk level.
As an example, consider the DMN model in Figure 1, which calculates a
BMI level and risk level based on a patient’s weight and height. The table on
the left consists of one rule, which is read as “For every possible weight and
height, the BMI is weight divided by height squared.” The table on the right
then decides what the value for BMI Level and Risk Level is, based on the BMI.
Using this model, standard DMN tools could for example calculate that for a
weight of 100kg and height of 1.8m, the BMI is 30.9, resulting in a high risk level.
However, say we now want to know the opposite, i.e., what weight would give a
1.8m patient a low risk level. Standard DMN tools cannot infer this information
from this model. Our tool on the other hand is capable of reasoning backwards,
even with incomplete data. This allows us to enter the height and set the value
of Risk Level to “Low” while leaving the weight unknown. By now maximizing
the Weight variable, we ﬁnd that a weight less than 80.7kg results in a low risk
for the height.
The tool also includes some basic functionality for detecting common errors
in DMN speciﬁcations. We plan to develop this further in future work, along
with a functionality to improve the traceability of decisions.
During the demo, participants will get to interact with our tool via multiple
use cases, allowing them to explore the capabilities of the system freely. They
will also be encouraged to experiment with the DMN models themselves, so that
they can learn the connections between the components. An online version of
the tool is available at https://autoconﬁg-dmn.herokuapp.com/.
1. Aerts, B., Vandevelde, S., Vennekens, J.: Tackling the dmn challenges with cdmn:
A tight integration of dmn and constraint reasoning. Springer (2020)
2. bpmn.io: Dmn viewer and editor. https://bpmn.io/toolkit/dmn-js/ (2015)
3. Carbonnelle, P., Aerts, B., Deryck, M., Vennekens, J., Denecker, M.: An interactive
consultant. In: Proceedings of the 31st Benelux Conference on Artiﬁcial Intelligence
(BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Bene-
learn 2019), Brussels, Belgium, November 6-8, 2019 (2019)
4. Dasseville, I., Janssens, L., Janssens, G., Vanthienen, J., Denecker, M.: Combining
dmn and the knowledge base paradigm for ﬂexible decision enactment (2016)
5. De Cat, B., Bogaerts, B., Bruynooghe, M., Janssens, G., Denecker, M.: Predicate
logic as a modeling language: The idp system. pp. 279–329. ACM Books (2018)
6. Deryck, M., Hasic, F., Vanthienen, J., Vennekens, J.: A case based inquiry into
the decision model and notation (dmn) and the knowledge base (kb) paradigm. In:
Proceedings of RuleML+RR 2018. vol. 11092 LNCS, pp. 248–263. Springer (2018)