Check out the competition details below and be sure to join our Discord server https://discord.gg/dA8jpFVa9t for questions or news

Motivation

Control systems play a vital role in managing and regulating various processes and devices across diverse application domains. Their pervasive nature makes them a cornerstone of modern technology. Safety-critical applications, in particular, demand control systems that are not only efficient but also interpretable, ensuring trustworthiness, reliability, and accountability. However, a prevalent issue in the field is the over-reliance on opaque systems, such as deep neural networks, known for their efficiency and optimization potential. This preference is rooted in the prevailing belief that interpretability is of secondary importance, with performance taking precedence. Furthermore, the scarcity of objective metrics to assess the degree of interpretability in a system exacerbates this problem. In fact, though the Evolutionary Computation (EC) community is starting to promote explainable and/or interpretable AI, significant challenges still persist in achieving comprehensive solutions. The goal of this competition is thus to ignite the research domain of interpretable control, with two specific goals in mind. First, we want to create a basis of comparison for different techniques emphasizing the trade-offs between performance and interpretability. Second, through the involvement of a panel of human evaluators, we strive to uncover the key characteristics that enhance the interpretability of control policies, making them more accessible to the general user.

Tasks

Building on previous editions, which explored both continuous and discrete control settings (from robotic locomotion to game-based environments), this year’s competition will take a step toward more realistic, safety-relevant scenarios. In collaboration with Airbus, a global leader in aerospace innovation and manufacturing, we will focus on a challenge related to aeronautical decision-making, focusing on relevant concrete scenarios that exemplify the complexity and reliability demands of real-world systems.

Participants will be welcome to enter the competition using their preferred methods to develop and interpret control policies for addressing the proposed task. We particularly encourage the incorporation of evolutionary computation techniques to enhance either policy generation or interpretability.

Submissions will be evaluated based on both performance and interpretability. Performance will be assessed through simulations of each submitted policy, while interpretability will be evaluated by a panel of judges, including domain experts from the industry.

✈️ Details on the competition task will be coming very soon, stay tuned! ✈️

If you want to ask any question or provide some feedback, join us at our Discord server: https://discord.gg/dA8jpFVa9t.

Submission deadline

1st June 2026 AoE.

Results and dissemination

Our objective is to raise awareness regarding the importance of interpretability within the realm of control systems. To achieve this, we aim at collecting a wide variety of methodologies and publishing the results in a comprehensive report. We also plan to extend an invitation to select participants to become co-authors of this publication.

Prize

The winner(s) will be awarded a certificate.

Organizers

Giorgia Nadizar, University of Trieste, giorgia.nadizar@irit.fr
Giorgia Nadizar is a postdoctoral researcher at the Toulouse Capitole University, France. Her research interests lie at the intersection of embodied AI and explainable/interpretable AI.

Luigi Rovito, University of Trieste, luigi.rovito@phd.units.it
Luigi Rovito is a third year PhD student at the University of Trieste, Italy. His research interests are genetic programming for cryptography and interpretable ML.

Dennis G. Wilson, ISAE-SUPAERO, University of Toulouse, dennis.wilson@isae.fr
Dennis G. Wilson is an Associate Professor at ISAE-Supaero in Toulouse, France. They research evolutionary algorithms, deep learning, and applications of AI to climate problems.

Eric Medvet, University of Trieste, emedvet@units.it
Eric Medvet is an Associate Professor at the University of Trieste, Italy. His research interests include embodied AI, artificial life, and evolutionary optimization.

Florent Teichteil Koenigsbuch, AIRBUS
Florent Teichteil Koenigsbuch is an engineer at AIRBUS, working at the frontier of operation research, artificial intelligence and applied mathematics.

Interpretable Control Competition