Combining Mental Models with Neural Networks

Paweł Mąka, Jelle Jansen, Theodor Antoniou, Thomas Bahne, Kevin Müller, Can Türktas, Nico Roos*, Kurt Driessens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Human reasoning under uncertainty is conjectured to use Mental Models as a representation format. Each Mental Model characterizes a possible state of the world based on and constrained by the available information. Conclusion about the world must hold in each of these Mental Models. An important task in human reasoning is the construction these Mental Models using the available information. This paper investigates whether it is possible to design a neural network architecture that enables the construction of Mental Model, similarly to the conjectured way that humans reason. The paper investigates different architectures in an incremental way. The final architecture not only produces the correct mental models but also learns correct mental models for intermediate representation without being explicitly trained to do so. This contributes to the explainability of the approach.
Original languageEnglish
Title of host publicationProceedings of BNAIC/BeneLearn 2021
Subtitle of host publication33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning BNAIC/BENELEARN
Number of pages15
Publication statusPublished - 2021
Event33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning - Belval, Esch-sur-Alzette, Luxembourg
Duration: 10 Nov 202112 Nov 2021
Conference number: 33


Conference33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Abbreviated titleBNAIC/BeneLearn 2021
CityBelval, Esch-sur-Alzette
Internet address


  • Machine Learning
  • Mental Models
  • Neural Networks
  • Reasoning

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