The workshop on
Spatio-Temporal Reasoning and Learning is organised as part of IJCAI 2023,
the 32nd International Joint Conference on Artificial Intelligence, the premier AI Research conference
(IJCAI 2023 - https://ijcai-23.org) bringing
together the international AI community to communicate the advances and achievements of artificial intelligence research. IJCAI 2023 is
held at Macao, China. The workshop will be held as a full-day event.
Opposing the false dilemma of logical reasoning vs machine learning, we argue for a synergy between these two paradigms in order to obtain hybrid, human-centred AI systems that will be robust, generalisable, explainable, and ecologically valid. Indeed, it is well-known that machine learning only includes statistical information and, therefore, on its own is inherently unable able to capture perturbations (interventions or changes in the environment), or perform explainable 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 available apriori. It is becoming ever more evident in the literature that modular AI architectures should be prioritised, 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.
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 formal methods developed within the spatial and temporal reasoning community, be it qualitative or quantitative, naturally build upon (commonsense) physics and human cognition, and could therefore form a module that would be beneficial towards causal representation learning. A (relational) spatio-temporal knowledge base could provide a foundation upon which machine learning models could generalise, and exploring this direction from various perspectives is the main theme of this workshop.
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 topics above are not exhaustive; we aim to foster the debate around all aspects of the suggested integration.
Application domains being addressed include, but are not limited to:
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 poster, and submissions that are at least 5 pages and up to 7 pages long (again, excluding references and appendices) will be considered for an oral 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). Workshop submissions will be handled by EasyChair; the submission link is as follows: https://easychair.org/conferences/?conf=strl2023
All questions about submissions should be emailed to the organizers.
Note: all deadlines are AoE (Anywhere on Earth).
The accepted papers will appear on the workshop website. Furthermore, we will also 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 2023 is a workshop, not a conference, submission of the same paper to conferences or journals is acceptable from our standpoint.
To be announced
Dr. Michael Sioutis is a Junior Professor in Hybrid Artificial Intelligence with LIRMM UMR 5506 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.
Dr. Jae Hee Lee is a Postdoc with the Knowledge Technology Group of the 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 within the School of Science and Technology at Örebro University, Sweden. His research focusses on the 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. Visuospatial cognition and computation has been an area of intense activity from the viewpoint of interdisciplinary research.
To be announced
Practical details and additional information about the exact venue will be announced in due course.