Knowledge Base System
Use your domain knowledge
to power intelligent systems.
Expressing your knowledge about a particular domain is much simpler than writing programs. IDP-Z3 understands knowledge expressed in familiar mathematical notation and easy-to-maintain Excel-like tables.
In our daily lives, we often use the same knowledge to perform very different tasks. IDP-Z3 also has the intelligence to re-use knowledge to solve very different problems.
IDP-Z3 uses logic to rigorously make inferences. There are no black-box models. Whenever IDP-Z3 gives you a solution, it can be explained.
FO(.) (aka FO-dot) is our knowledge representation language based on first-order logic, with extensions to make it more expressive: types, (inductive) definitions, aggregates, and partial functions.Learn more
cDMN is an extension of the Decision Modelling Notation (DMN) standard. It facilitates the maintenance of knowledge bases by end-users.Learn more
IDP-Z3 is the reasoning engine for FO(.) and cDMN. It can perform a variety of reasoning tasks by re-using the same knowledge base.Learn more
Our Interactive Consultant (IC) helps end-users make reasonable decisions, fast. It is configured by entering the knowledge of a particular domain. No programming required !Learn more
Our technology is deployed in finance, engineering and legal applications.
Our technology is based on research done at KU Leuven and published in award-winning papers:
- Interactive Feature Modeling with Background Knowledge for Validation and Configuration, Simon Vandevelde, et al. (2022).
Best PhD Paper Award at ConfWS 2022
On the relationship between Approximation Fixpoint Theory and Justification Theory, Simon Marynissen, et al. (2021).
Distinguished Paper Award at IJCAI 2021
Tackling the DMN challenges with cDMN: A tight integration of DMN and constraint reasoning. Aerts B., et al. (2020).
Best Paper Award at RuleML+RR 2020
Ultimate Well-Founded and Stable Semantics for Logic Programs with Aggregates. Denecker, et al. (2001).
20-Year Test of Time Award at ICLP 2021
Extending classical logic with inductive definitions. Denecker M. (2000) ICCL2020.
20 Year Test of Time Award at ICLP 2020
Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment, Dasseville et al. (2016)
Winner of RuleML 2016 Challenge