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[Apologies if you receive multiple copies]</div>
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CALL FOR PAPERS</div>
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The 5th Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2024)</div>
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will be held in Hanoi, Vietnam., between November 2 and 4, 2024, see</div>
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https://sites.google.com/view/xlokr2024</div>
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As in previous years, it will be co-located with KR 2024 (https://kr.org/KR2024).</div>
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Description</div>
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Embedded or cyber-physical systems that interact autonomously with the</div>
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real world, or with users they are supposed to support, must continuously</div>
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make decisions based on sensor data, user input, knowledge they have</div>
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acquired during runtime as well as knowledge provided during design-time.</div>
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To make the behavior of such systems comprehensible, they need to be</div>
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able to explain their decisions to the user or, after something has</div>
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gone wrong, to an accident investigator.</div>
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While systems that use Machine Learning (ML) to interpret sensor</div>
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data are very fast and usually quite accurate, their decisions are</div>
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notoriously hard to explain, though huge efforts are currently being</div>
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made to overcome this problem. In contrast, decisions made by</div>
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reasoning about symbolically represented knowledge are in principle</div>
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easy to explain. For example, if the knowledge is represented in (some</div>
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fragment of) first-order logic, and a decision is made based on the result</div>
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of a first-order reasoning process, then one can in principle use a formal</div>
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proof in an appropriate calculus to explain a positive reasoning result,</div>
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and a counter-model to explain a negative one. In practice, however, things</div>
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are not so easy also in the symbolic KR setting. For example, proofs and</div>
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counter-models may be very large, and thus it may be hard to comprehend</div>
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why they demonstrate a positive or negative reasoning result, in particular</div>
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for users that are not experts in logic. Thus, to leverage explainability as</div>
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an advantage of symbolic KR over ML-based approaches, one needs to ensure</div>
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that explanations can really be given in a way that is comprehensible to</div>
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different classes of users (from knowledge engineers to laypersons).</div>
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The problem of explaining why a consequence does or does not follow from a</div>
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given set of axioms has been considered for full first-order theorem proving</div>
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since at least 40 years, but there usually with mathematicians as users in</div>
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mind. In knowledge representation and reasoning, efforts in this direction</div>
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are more recent, and were usually restricted to sub-areas of KR such as AI</div>
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planning and description logics. The purpose of this workshop is to bring</div>
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together researchers from different sub-areas of KR and automated deduction</div>
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that are working on explainability in their respective fields, with the goal</div>
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of exchanging experiences and approaches.</div>
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Topics of Interest</div>
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A non-exhaustive list of areas to be covered by the workshop are the following:</div>
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* AI planning</div>
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* Answer set programming</div>
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* Argumentation frameworks</div>
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* Automated reasoning</div>
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* Causal reasoning</div>
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* Constraint programming</div>
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* Description logics</div>
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* Non-monotonic reasoning</div>
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* Probabilistic representation and reasoning</div>
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IMPORTANT DATES</div>
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Paper submission deadline: July 17, 2024</div>
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Notification: August 21, 2024</div>
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Workshop date: between November 2 and 4, 2024</div>
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AUTHOR GUIDELINES AND SUBMISSION INFORMATION</div>
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We invite extended abstracts of 2-5 pages on topics related to explanation</div>
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in logic-based KR. The papers should be formatted in Springer LNCS Style</div>
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and can be submitted via EasyChair to</div>
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https://easychair.org/conferences/?conf=xlokr2024</div>
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Since the workshop will only have informal proceedings and the main purpose is</div>
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to exchange results, we welcome not only papers covering unpublished results,</div>
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but also previous publications that fall within the scope of the workshop.</div>
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