4th International Workshop on Spatio-Temporal Reasoning and Learning

Introduction

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:

Objective

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

Topics

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:

Real-world problems / applications involving spatio-temporal data
Spatial, temporal, and spatio-temporal knowledge graphs
Spatio-temporal data mining / analysis
Space and time in narratives
Declarative spatial reasoning
Spatial and temporal language understanding with and without additional modalities (e.g., vision)
(Multimodal) large language models and spatiotemporal reasoning
Neuro-symbolic approaches for spatio-temporal reasoning and learning
Probabilistic world models for spatio-temporal reasoning and learning
Probabilistic inference for spatio-temporal reasoning and learning
Datasets for spatio-temporal reasoning and learning
Metrics for assessing spatio-temporal reasoning and learning methods
Limitations in machine learning for spatio-temporal reasoning and learning; how far can machine learning go?
Relation between causal reasoning and spatial and temporal reasoning
Research and teaching challenges in spatio-temporal reasoning and learning

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:

Autonomous Vehicles and Drones
Cognitive Robotics
Spatial Computing for Design
Computational Art
(Cognitive) Vision
Geographic Information Systems
Smart Environments
Healthcare

Submission

The submission link will be available soon.

Guidelines

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 TBD.

Important Dates

TBD

Proceedings

The accepted papers will appear on the workshop website. We also intend to publish the workshop proceedings with CEUR-WS.org; this option will be discussed with the authors of accepted papers and is subject to the CEUR-WS.org preconditions. We note that, as STRL 2025 is a workshop, not a conference, submission of a revised and/or extended version of a paper to conferences or journals is acceptable from our standpoint.

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Workshop Program

Speakers

TBD

Schedule

TBD

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Workshop Organization

Organizing Committee

	Prof. Parisa Kordjamshidi is an Associate 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.
	Assoc. Prof. Zhiguo Long is an Associate Professor 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 data representation and clustering in Machine Learning.
	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. Xun Gong is a Professor and an Associate Dean of the School of Computing and Artificial Intelligence, Southwest Jiaotong University. He received the Ph.D. degree in computer science and technology from Southwest Jiaotong University in 2008. His research interests include pattern recognition, computer vision, medical image processing, and deep learning.

Advisory Committee

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.

Program Committee

TBD

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Venue

TBD

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