The DMN standard allows users to build declarative models of their decision knowledge. The standard aims at being simple enough to allow business users to construct these models themselves, without help from IT staff. To this end, it combines simple decision tables with a clear visual notation. However, for real-life applications, DMN sometimes proves too restrictive. In this paper, we develop an extension to DMN's decision table notation, which allows more knowledge to be expressed, while retaining the simplicity of DMN. We demonstrate our new notation on a real-life case study on product design.

Recently, a prototype for an interactive decision enactment system for notaries was developed. This prototype follows the Knowledge Base Paradigm (KBP): it consists of purely declarative domain knowledge, to which various logical inference methods can be applied. This paper extends that work in two ways. First, we experimentally validate the claim that the KBP leads to highly maintainable software. Second, we extend the number of additional logical inferences, which allow us to address a number of usability concerns. This provides further evidence for the claim that the KBP is indeed a viable method of developing interactive software systems. The resulting decision enactment prototype is a fully generic system, that can be applied to other domains with minimal effort.

Modelling decisions in organisations is a challenging task. Deciding which modelling language to use for the problem at hand is a fundamental question. We investigate the Decision Model and Notation (DMN) standard and the IDP knowledge base system (KBS) in their effectiveness to model and solve specific real-life case problems. This paper presents two cases that are solved with DMN and IDP: (1) Income taxation for foreign artists temporarily working in Belgium; and (2) Registration duties when purchasing real-estate in Belgium. DMN is used as a front-end method, assisting the business analyst in the analysis and modelling of the business domain and communication with the domain expert. It is complemented with the representation of the logic in IDP as back-end system, which allows more forms of inference.

This paper presents an application that we developed to assist users with the creation of an investment profile for the selection of financial assets. It consists of a natural language interface, an automatic translation to a declarative FO(.) knowledge base, and the IDP reasoning engine with multiple forms of logical inference. The application speeds up the investment profile creation process, and reduces the considerable inherent operational risk linked to the creation of investment profiles

This paper studies Knowledge Bases (KBs) in PSOA RuleML and IDP, aligning, interoperating, and co-executing them for a use case of Air Traffic Control (ATC) regulations. We focus on the common core of facts and rules in both languages, explaining basic language features. The used knowledge sources are regulations that are specified in (legal) English, and an aircraft data schema. In the modeling process, inconsistencies in both sources were discovered. We present the discovery process utilizing both specification languages, and highlight their unique features. We introduce three extensions to this ATC KB core: 1) While the current PSOA RuleML does not distinguish the ontology separately from the instance level, IDP does. Hence we specify a vocabulary-enriched version of ATC KB in IDP for knowledge validation. 2) While the current IDP uses relational modeling, PSOA also supports graph modeling. Hence we specify a relationally interoperable graph version of ATC KB in PSOA.3) The KB is extended to include optimization criteria. With this, the determination of an optimal sequence of more than two aircraft is possible.