About

Automated Reasoning for Space (AR4Space) is a seminar and workshop series that brings together researchers in automated reasoning in the broader sense (including formal methods, constraint satisfaction, logic-based optimization, probabilistic and non-monotonic reasoning, neuro-symbolic reasoning, knowledge representation and compilation, automated planning, argumentation, and related areas) with engineers and practitioners from industry, academia, and space agencies working on the design and operation of future space missions.

As space missions rapidly grow in scale, autonomy, and complexity, there is an increasing need for rigorous, reliable, and intelligent decision-making tools. The primary goal of AR4Space is to bridge the automated reasoning and space engineering communities by fostering a shared vocabulary, exchanging challenges and lessons learned, and adapting state-of-the-art reasoning methods to the dynamic and safety-critical problems arising in future space missions.

The frequency, duration, and format of seminars and workshops within the series are determined by the steering committee, depending on the topic and community interest. In the list below, you will find an overview of past and upcoming AR4Space events. While most events are invitation-based to facilitate focused discussion, we encourage you to contact the steering committee if you would like to participate in a future event or become involved in organizing an edition of the series.

Scope of the AR4Space Series

The scope of AR4Space is intentionally broad, reflecting both the methodological diversity of automated reasoning and the technical breadth of modern space missions. Topics range from on-board reasoning and autonomous decision-making, formal verification and validation of AI-based systems for space applications, and automated mission planning, to large-scale optimization challenges such as multi-flyby and multi-rendezvous trajectory design. Both theoretical and foundational contributions (such as computational complexity, algorithm design, modeling techniques, and problem encodings) and application-driven case studies addressing concrete mission scenarios are equally welcome.