Opposing the false dilemma of logical reasoning vs machine learning, we argue for a synergy between these two paradigms in order to obtain hybrid AI systems that will be robust, generalizable, and transferable.

Indeed, it is well-known that machine learning only includes statistical information and, therefore, is not inherently able to capture perturbations (interventions or changes in the environment), or perform reasoning and planning. Ideally, (the training of) machine learning models should be tied to assumptions that align with physics and human cognition to allow for these models to be re-used and re-purposed in novel scenarios.

On the other hand, it is also the case that logic in itself can be brittle too, and logic further assumes that the symbols with which it can reason are already given.

It is becoming ever more evident in the literature that modular AI architectures should be prioritized, where the involved knowledge about the world and the reality that we are operating in is decomposed into independent and recomposable pieces, as such an approach should only increase the chances that these systems behave in a causally sound manner.

You may find details about previous editions of this workshop via the links below:


The aim of this workshop is to formalize such a synergy between logical reasoning and machine learning that will be grounded on spatial and temporal knowledge.

We argue that the calculi associated with the spatial and temporal reasoning community, be it qualitative or quantitative, naturally build upon physics and human cognition, and could therefore form a module that would be beneficial towards causal representation learning. A (symbolic) spatio-temporal knowledge base could provide a dependable causal seed upon which machine learning models could generalize, and exploring this direction from various perspectives is the main theme here.

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Call For Papers


In this workshop, we invite the research community in artificial intelligence to submit works related to the proposed integration of spatial and temporal reasoning with machine learning, revolving around the following topic areas:

The list above is by no means exhaustive, as the aim is to foster the debate around all aspects of the suggested integration.

Application domains being addressed include, but are not limited to:


The submission link is available at


Papers should be formatted according to the CEUR-ART style formatting guidelines here and submitted as a single PDF file.

We welcome submissions across the full spectrum of theoretical and practical work including research ideas, methods, tools, simulations, applications or demos, practical evaluations, and surveys.

Submissions that are 2 pages long (excluding references and appendices) will be considered for a short presentation, and submissions that are between 4 and 7 pages long (again, excluding references and appendices) will be considered for a regular presentation.

All papers will be peer-reviewed in a single-blind process and assessed based on their novelty, technical quality, potential impact, clarity, and reproducibility (when applicable).

All questions about submissions should be emailed to the organizers via strl2024 at

Important Dates

Be mindful of the following dates:

Note: all deadlines are AoE (Anywhere on Earth).


The accepted papers will appear on the workshop website. We also intend to publish the workshop proceedings with; this option will be discussed with the authors of accepted papers and is subject to the preconditions. We note that, as STRL 2024 is a workshop, not a conference, submission of the same paper to conferences or journals is acceptable from our standpoint.

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Workshop Organization

Organizing and Advisory Committee

Prof. Parisa Kordjamshidi is an Assistant Professor with the Department of Computer Science and Engineering of Michigan State University, US. Her main research interests are artificial intelligence, machine learning, natural language processing, and declarative learning based programming (DeLBP). Parisa is directing the research lab on Heterogeneous Learning and Reasoning.
Dr. Jae Hee Lee is a Postdoctoral Research Associate with the Knowledge Technology Group, University of Hamburg, Germany. His research aims to develop deep learning models for language understanding by leveraging multimodal information (e.g., vision, proprioception) with a particular focus on robustness and explainability.
Prof. Mehul Bhatt is a Professor of Computer Science with the School of Science and Technology at Örebro University, Sweden. His basic research focuses on formal, cognitive, and computational foundations for AI technologies with a principal emphasis on knowledge representation, semantics, integration of commonsense reasoning & learning, explainability, and spatial representation and reasoning. Mehul steers CoDesign Lab, and directs the research and consulting group DesignSpace.
Prof. Michael Sioutis is a Junior Professor of Hybrid AI with the Laboratory of Computer Science, Robotics, and Microelectronics of Montpellier and the Faculty of Sciences of the University of Montpellier, France. His general interests lie in artificial intelligence, knowledge representation and reasoning, data mining, logic programming, and semantic web.
Dr. Zhiguo Long is a Lecturer with the School of Computing and Artificial Intelligence of the Southwest Jiaotong University, Chengdu, China. His research interests include fundamental and practical techniques in knowledge representation and reasoning, especially in qualitative spatial and temporal reasoning, and topological data analysis.

Program Committee (Tentative)

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The workshop will take place in Jeju island, South Korea, co-located with IJCAI 2024; more details will be provided in due time.

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