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The Fourth Workshop on Explainable Logic-Based Knowledge Representation
Sponsored by The Transregional Collaborative Research Centre 248 “Foundations of Perspicuous Software Systems” (CPEC – TRR 248).
Schedule
Schedule
About
About
XLoKR 2023 is the fourth workshop in the XLoKR series. It will be co-located with the 20th International Conference on Principles of Knowledge Representation and Reasoning, which will take place in Rhodes, Greece. Previous editions 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 and
2022 co-located with the 19th International Conference on Principles of Knowledge Representation and Reasoning.
Motivation and Topic
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 there 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
Important Dates
Paper Submission: June 11, 2023 (extended)
Notification: July 4, 2023
Camera-ready papers: August 04, 2023
September 2, 2023
Submission Guidelines
Submission Guidelines
We invite extended abstracts of 2-5 pages (excluding references) on topics related to explanation in logic-based KR. The papers should be formatted in Springer LNCS Style and must be submitted via EasyChair to the KR 2023 special track "Workshop on Explainable Logic-Based Knowledge Representation"
https://easychair.org/conferences/?conf=kr2023
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.
Organization
Organization
Organization Committee
Organization Committee
Franz Baader, TU Dresden
Bart Bogaerts, Vrije Universiteit Brussel
Gerd Brewka, University of Leipzig
Jörg Hoffmann, Saarland University
Thomas Lukasiewicz, University of Oxford
Nico Potyka, Cardiff University
Francesca Toni, Imperial College London
Program Committee
Program Committee
Hamed Ayoobi, Imperial College London
Franz Baader, TU Dresden
Sander Beckers, University of Tübingen
Bart Bogaerts, Vrije Universiteit Brussel (VUB)
Annemarie Borg, Utrecht University
Alex Borgida, Rutgers University
Stefan Borgwardt, TU Dresden
Gerhard Brewka, Leipzig University
Sarah Alice Gaggl, TU Dresden
Ruth Hoffmann, University of St Andrews, School of Computer Science
Joerg Hoffmann, Saarland University
Francesco Leofante, Imperial College London
Thomas Lukasiewicz, University of Oxford
Pierre Marquis, CRIL, U. Artois & CNRS - Institut Universitaire de France
Cristian Molinaro, Università della Calabria
Ana Ozaki, University of Bergen
Guilherme Paulino-Passos, Imperial College London
Rafael Peñaloza, University of Milano-Bicocca
Nico Potyka, Imperial College London
Antonio Rago, Imperial College London
Silja Renooij, Utrecht University
Francesco Ricca, Department of Mathematics and Computer Science University of Calabria
Tjitze Rienstra, Maastricht University
Zeynep G. Saribatur, TU Wien
Stefan Schlobach, Vrije Universiteit Amsterdam
Mohan Sridharan, University of Birmingham
Francesca Toni, Imperial College London
Trung-Kien Tran, Bosch Center for Artificial Intelligence
Markus Ulbricht, University of Leipzig
Contact
Contact
For organizational questions, please contact Nico Potyka.
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