XLoKR 2025

Sponsored by the Argumentation-based Deep Interactive EXplanations project under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101020934) and the Transregional Collaborative Research Centre 248 “Foundations of Perspicuous Software Systems” (CPEC – TRR 248).

Invited Speaker

Accepted Papers

• Christian Alrabbaa, Stefan Borgwardt, Philipp Herrmann and Markus Krötzsch
  The Shape of EL Proofs: A Tale of Three Calculi (Extended Abstract)

• Lars Bengel and Matthias Thimm
  Sequence Explanations for Acceptance in Abstract Argumentation

• Meghyn Bienvenu, Diego Figueira and Pierre Lafourcade
  Shapley Revisited: Tractable Responsibility Measures for Query Answers (Extended Abstract)

• Martin Demovič, Peter Švec, Martin Homola and Maurice Funk
  On the Prospects of EL and ELU Concept Learners for Explainable Malware Detection

• Tobias Geibinger and Zeynep G. Saribatur
  What Can We Explain in Answer Set Programming?

• Nicola Gigante, Francesco Leofante and Andrea Micheli
  Counterfactual Scenarios for Automated Planning

• Jakub Kloc, Janka Boborová, Martin Homola and Júlia Pukancová
  CATS Solver: The Rise of Hybrid Abduction Algorithms (Extended Abstract)

• Rafael Patronilo, Ricardo Gonçalves, Matthias Knorr and Ludwig Krippahl
  Towards Embedding Concepts into Neural Networks for Explanations

About

XLoKR 2025 is the sixth workshop in the XLoKR series. It will be co-located with the 22nd International Conference on Principles of Knowledge Representation and Reasoning, which will take place in Melbourne, Australia. Previous editions of XLoKR took place in

• 2020 co-located with the 17th International Conference on Principles of Knowledge Representation and Reasoning,

• 2021 co-located with the 18th International Conference on Principles of Knowledge Representation and Reasoning,

• 2022 co-located with the 19th International Conference on Principles of Knowledge Representation and Reasoning,

• 2023 co-located with the 20th International Conference on Principles of Knowledge Representation and Reasoning and

• 2024 co-located with the 21st International Conference on Principles of Knowledge Representation and Reasoning.

Motivation and Topic

Embedded or cyber-physical systems that interact autonomously with the real world, or with users they are supposed to support, must continuously make decisions based on sensor data, user input, knowledge they have acquired during runtime as well as knowledge provided during design-time. To make the behavior of such systems comprehensible, they need to be able to explain their decisions to the user or, after something has gone wrong, to an accident investigator.

While systems that use Machine Learning (ML) to interpret sensor data are very fast and usually quite accurate, their decisions are notoriously hard to explain, though huge efforts are currently being made to overcome this problem. In contrast, decisions made by reasoning about symbolically represented knowledge are in principle easy to explain. For example, if the knowledge is represented in (some fragment of) first-order logic, and a decision is made based on the result of a first-order reasoning process, then one can in principle use a formal proof in an appropriate calculus to explain a positive reasoning result, and a counter-model to explain a negative one. In practice, however, things are not so easy also in the symbolic KR setting. For example, proofs and counter-models may be very large, and thus it may be hard to comprehend why they demonstrate a positive or negative reasoning result, in particular for users that are not experts in logic. Thus, to leverage explainability as an advantage of symbolic KR over ML-based approaches, one needs to ensure that explanations can really be given in a way that is comprehensible to different classes of users (from knowledge engineers to laypersons).

The problem of explaining why a consequence does or does not follow from a given set of axioms has been considered for full first-order theorem proving since at least 40 years, but usually with mathematicians as users in mind. In knowledge representation and reasoning, efforts in this direction are more recent, and were usually restricted to sub-areas of KR such as AI planning and description logics. The purpose of this workshop is to bring together researchers from different sub-areas of KR and automated deduction that are working on explainability in their respective fields, with the goal of exchanging experiences and approaches. A non-exhaustive list of areas to be covered by the workshop are the following:

• AI planning

• Answer set programming

• Argumentation frameworks

• Automated reasoning

• Description logics

• Non-monotonic reasoning

• Probabilistic representation and reasoning

Important Dates

• Paper Submission: July 31, 2025

• Notification: August 21, 2025

• Camera-ready papers: October 10, 2025

• Workshop: November 13, 2025

Submission Guidelines

We invite extended abstracts of 2–5 pages (excluding references) on topics related to explanation in logic-based KR. Reviewing will be single-blind. The papers should be formatted in Springer LNCS Style and must be submitted via 

Easychair

Since the workshop will only have informal proceedings and the main purpose is to exchange results, we welcome not only papers covering unpublished results, but also previous publications that fall within the scope of the workshop. To avoid conflicts with previous/future publications, we will not have formal proceedings, but will make the papers available on the website.

Organization

Organization Committee

• Xiang Yin, Imperial College London 

• Stefan Borgwardt, TU Dresden

• Franz Baader, TU Dresden

• Nico Potyka, Cardiff University 

Steering Committee

• Franz Baader, TU Dresden

• Bart Bogaerts, Vrije Universiteit Brussel

• Jörg Hoffmann, Saarland University

• Thomas Lukasiewicz, University of Oxford

• Nico Potyka, Cardiff University 

• Francesca Toni, Imperial College London 

Program Committee

• Franz Baader, TU Dresden

• Bart Bogaerts, KU Leuven

• Stefan Borgwardt, TU Dresden

• Gerhard Brewka, Leipzig University

• Roberta Calegari, Università di Bologna

• Deniz Gorur, Imperial College London

• Jörg Hoffmann, Saarland University

• Ruth Hoffmann, University of St. Andrews

• Francesco Leofante, Imperial College London

• Thomas Lukasiewicz, Vienna University of Technology

• Pierre Marquis, CRIL, U. Artois & CNRS

• Cristian Molinaro, Università della Calabria

• Cem Okulmus, Paderborn University

• Rafael Peñaloza, University of Milano-Bicocca

• Nico Potyka, Cardiff University

• Antonio Rago, Imperial College London

• Francesco Ricca, University of Calabria

• Maryam Rostamigiv, University of Regina

• Fabrizio Russo, Imperial College London

• Zeynep G. Saribatur, TU Wien

• Stefan Schlobach, Vrije Universiteit Amsterdam

• Khan Shakil, University of Regina

• Xiang Yin, Imperial College London

Contact

For organizational questions, please contact Xiang Yin and Stefan Borgwardt.