The IDP
Knowledge Base System
Use your domain knowledge
to power intelligent systems.
Why IDP-Z3?
Simple
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.
Versatile
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.
Explainable
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.
Core Technologies
FO(.)
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
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
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
The IC
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 moreFor industry
Our technology is deployed in finance, engineering and legal applications.
Our experts feel more confident in their decision-making when assisted by the Interactive Consultant.
We are looking forward to continue working with the professional and highly skilled team at KU Leuven.
– Bart Coppens, Intelli-Select
Award-winning research
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