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A Multifunctional, Interactive DMN Decision Modelling Tool

Authors:

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 interactive interface. 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 [6]. 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 [4], 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 [5]. 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 [2] and the IDP-based Interactive Consultant interface [3]. 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 Interactive Consultant interface. The translation from DMN to IDP is done by the same transformation used in the cDMN framework [1]. 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.
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
{s.vandevelde, joost.vennekens}@kuleuven.be
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-
tive interface.
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 [6]. 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 [4], 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 [5]. 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 [2] and the IDP-based Interactive Consultant interface
[3]. 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 [1]. 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
1<18.5Underweight Increased
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/.
References
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)
Preprint
Full-text available
FO(.) (aka FO-dot) is a language that extends classical first-order logic with constructs to allow complex knowledge to be represented in a natural and elaboration-tolerant way. IDP-Z3 is a new reasoning engine for the FO(.) language: it can perform a variety of generic computational tasks using knowledge represented in FO(.). It supersedes IDP3, its predecessor, with new capabilities such as support for linear arithmetic over reals and quantification over concepts. We present four knowledge-intensive industrial use cases, and show that IDP-Z3 delivers real value to its users at low development costs: it supports interactive applications in a variety of problem domains, with a response time typically below 3 seconds.
Chapter
Decision Model and Notation (DMN) models are user-friendly representations of decision logic. While the knowledge in the model could be used for multiple purposes, current DMN tools typically only support a single form of inference. We present DMN-IDPy, a novel Python API that links DMN as a notation to the IDP system, a powerful reasoning tool, allowing the knowledge in DMN models to be used to its fullest potential. The flexibility of this approach allows us to build intelligent tools based on DMN unlike any other execution engine.
This book constitutes the proceedings of the International Joint Conference on Rules and Reasoning, RuleML+RR 2021, held in Leuven, Belgium, during September, 2021. This is the 5th conference of a new series, joining the efforts of two existing conference series, namely “RuleML” (International Web Rule Symposium) and “RR” (Web Reasoning and Rule Systems). The 17 full research papers presented together with 2 short technical communications papers and 2 abstracts of invited papers were carefully reviewed and selected from 39 submissions.
Article
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.
Preprint
Full-text available
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.
Conference Paper
Full-text available
This paper describes an extension to the Decision Model and Notation (DMN) standard, called cDMN. DMN is a user-friendly, table-based notation for decision logic. cDMN aims to enlarge the expressivity of DMN in order to solve more complex problems, while retaining DMN’s goal of being readable 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, both in readability and compactness. Moreover, cDMN is able to solve more challenges than any other approach.
An interactive consultant
  • P Carbonnelle
  • B Aerts
  • M Deryck
  • J Vennekens
  • M Denecker
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 (Benelearn 2019), Brussels, Belgium, November 6-8, 2019 (2019)
Combining dmn and the knowledge base paradigm for flexible decision enactment
  • I Dasseville
  • L Janssens
  • G Janssens
  • J Vanthienen
  • M Denecker
Dasseville, I., Janssens, L., Janssens, G., Vanthienen, J., Denecker, M.: Combining dmn and the knowledge base paradigm for flexible decision enactment (2016)
A case based inquiry into the decision model and notation (dmn) and the knowledge base (kb) paradigm
  • M Deryck
  • F Hasic
  • J Vanthienen
  • J Vennekens
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)