6-8 Nov 2021
**Submission deadline** (extended abstracts): July 2 ** Update: Invited Speakers: ** Sheila McIlraith and Joe Halpern will deliver the keynotes at the workshop. CALL FOR PAPERS 2nd Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2021) co-located with KR 2021 https://kr2021.kbsg.rwth-aachen.de 6-8 November 2021 (exact date(s) TBD), Hanoi, Vietnam (virtually) https://xlokr21.ai.vub.ac.be/ ** Update: Invited Speakers: ** Sheila McIlraith and Joe Halpern will deliver the keynotes at the workshop. 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 * Causal reasoning * Constraint programming * Description logics * Non-monotonic reasoning * Probabilistic representation and reasoning ** IMPORTANT DATES ** Paper submission deadline: July 2, 2021 Notification: August 6, 2021 Workshop dates: November 6-8, 2021 (exact date TBD) **AUTHOR GUIDELINES AND SUBMISSION INFORMATION** Researchers interested in participating in the workshop should submit extended abstracts of 2-5 pages on topics related to explanation in logic-based KR. The papers should be formatted in Springer LNCS Style and must be submitted via EasyChair https://easychair.org/conferences/?conf=xlokr21. The workshop will have informal proceedings, and thus, in addition to new work, also papers covering results that have recently been published or will be published at other venues are welcome. -- [LOGIC] mailing list http://www.dvmlg.de/mailingliste.html Archive: http://www.illc.uva.nl/LogicList/ provided by a collaboration of the DVMLG, the Maths Departments in Bonn and Hamburg, and the ILLC at the Universiteit van Amsterdam