Belgian Evolutionary and Hybrid AI Foundation


The Belgian Evolutionary and Hybrid AI Foundation (BEEHAIF) is a Belgian consortium of artificial intelligence researchers who focus on building truly intelligent systems. Such systems are able to observe, understand, reason about and act upon their native environment. Rather than focussing on a specific task or technique, BEEHAIF researchers develop a wide spectrum of methods that can flexibly be combined in order to solve an open-ended set of tasks. Methodologically, BEEHAIF researchers are united in their belief that evolutionary principles and a combination of symbolic and subsymbolic AI techniques are the key to achieving human-like artificial intelligence. Apart from fostering collaboration between researchers, organising community-wide events and increasing the visibility of the evolutionary and hybrid AI research programme, the BEEHAIF also plays a crucial role in supporting the development of a range of open-source software libraries. Most prominently, BEEHAIF fellows are leading the development of the Babel toolkit for implementing multi-agent experiments on emergent communication and the Fluid Construction Grammar (FCG) Editor for representing and processing computational construction grammars. If you would like to connect with the BEEHAIF community, feel free to reach out so that we can discuss potential collaborations.

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Selected Publications

  • All
  • evolutionary AI
  • hybrid AI
  • computational construction grammar

The FCG Editor: An innovative environment for engineering computational construction grammars

Remi van Trijp, Katrien Beuls and Paul Van Eecke

PLOS ONE 17(6), 2022, e0269708.

This paper introduces the FCG Editor, a dedicated and innovative integrated development environment for the Fluid Construction Grammar formalism. Offering a straightforward installation and a user-friendly, interactive interface, the FCG Editor is an accessible, yet powerful tool for construction grammarians who wish to operationalise their construction grammar insights and analyses in order to computationally verify them, corroborate them with corpus data, or integrate them in language technology applications.

Paper Bibtex PLOS ONE

Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning

Paul Van Eecke and Katrien Beuls

The 2020 AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL). ArXiv 2004.04722.

In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL).

Paper Bibtex ArXiv

From Continuous Observations to Symbolic Concepts: A Discrimination-Based Strategy for Grounded Concept Learning

Jens Nevens, Paul Van Eecke and Katrien Beuls

Frontiers in Robotics and AI 7, 2020, 10.3389/frobt.2020.00084

In this paper, we introduce a novel methodology for grounded concept learning. In a tutor-learner scenario, the method allows an agent to construct a conceptual system in which meaningful concepts are formed by discriminative combinations of prototypical values on human-interpretable feature channels.

Paper Bibtex Google Scholar

A Practical Guide to Studying Emergent Communication through Grounded Language Games

Jens Nevens, Paul Van Eecke and Katrien Beuls

Proceedings of AISB '19 , Falmouth, United Kingdom, 2019, 1-8.

The aim of this paper is twofold. On the one hand, it introduces a high-level robot interface that extends the Babel software system, presenting for the first time a toolkit that provides flexible modules for dealing with each subtask involved in running advanced grounded language game experiments. On the other hand, it provides a practical guide to using the toolkit for implementing such experiments, taking a grounded colour naming game experiment as a didactic example.

Paper Bibtex Google Scholar

Computational Construction Grammar for Visual Question Answering

Jens Nevens, Paul Van Eecke and Katrien Beuls

Linguistics Vanguard 5(1), 2019, 2018-0070, 1-16.

This paper focuses on the first capability, presenting a novel approach to semantically parsing questions expressed in natural language. The method makes use of a computational construction grammar model for mapping questions onto their executable semantic representations. We demonstrate and evaluate the methodology on the CLEVR visual question answering benchmark task.

Paper Bibtex Google Scholar

Exploring the Creative Potential of Computational Construction Grammar

Paul Van Eecke and Katrien Beuls

Zeitschrift für Anglistik und Amerikanistik 66(3), 2018, 341-355.

This paper discusses the computational mechanisms that allow construction grammar models to exhibit, to a certain extent, the creativity and inventiveness that is observed in human language use.

Paper Bibtex Google Scholar

Meta-Layer Problem Solving for Computational Construction Grammar

Paul Van Eecke and Katrien Beuls

2017 AAAI Spring Symposium Series, 2017, 258-265.

This paper presents a meta-layer architecture that is fully integrated into the FCG language processing framework, as well as a number of powerful and general operators that can be used within this architecture for on-the-fly problem solving and learning of lexical and grammatical constructions.

Paper Bibtex Google Scholar


Prof. dr. Katrien Beuls

Université de Namur

Assistant professor in artificial intelligence, focussing on unravelling the mechanisms underlying human-like communication.

Dr. Paul Van Eecke

Vrije Universiteit Brussel and KU Leuven

FWO postdoctoral fellow focussing on the emergence, evolution and acquisition of conceptual and linguistic structures in multi-agent systems.

Dr. Remi van Trijp

Sony Computer Science Laboratories Paris

Head of the Language Research Unit, combining techniques from computational linguistics, artificial intelligence and robotics to investigate human language processing.

Dr. Tom Willaert

Vrije Universiteit Brussel

Postdoctoral researcher in digital methods, bridging gaps between computational techniques and humanities interpretative practice to analyze online (mis)information.

Jens Nevens

Vrije Universiteit Brussel

FWO PhD fellow investigating how intelligent agents can represent and learn linguistic structures through situated, communicative interactions.

Lara Verheyen

Vrije Universiteit Brussel

PhD researcher on the Flanders AI research programme, using hybrid techniques to build intelligent agents that can hold meaningful conversations with humans.

Jonas Doumen

KU Leuven

PhD researcher at Itec, imec research group at KU Leuven, focussing on computationally modelling the mechanisms underlying human language acquisition.

Jérôme Botoko Ekila

Vrije Universiteit Brussel

PhD researcher at the AI Lab of the VUB, re-conceptualising the language game paradigm within the framework of multi-agent reinforcement learning.

Veronica Juliana Schmalz

KU Leuven

PhD researcher at Itec, imec research group at KU Leuven, investigating the acquisition of modular computational construction grammars.

Liesbet De Vos

Université de Namur

PhD researcher in multi-modal computational construction grammar funded by the Walloon flagship project on AI (ARIAC).

Belgian Evolutionary and Hybrid AI Foundation

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