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<div class="ContentPasted0">[Apologies if you receive multiple copies]<br>
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<div class="ContentPasted0">CALL FOR PAPERS</div>
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<div class="ContentPasted0">The 4th Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2023)</div>
<div class="ContentPasted0">will be held in Rhodes, Greece, on September 2, 2023, see</div>
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<div class="ContentPasted0"> https://sites.google.com/view/xlokr2023/</div>
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<div class="ContentPasted0">It will be co-located with KR 2023 (https://kr.org/KR2023).</div>
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<div class="ContentPasted0">Description</div>
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<div class="ContentPasted0">Embedded or cyber-physical systems that interact autonomously with the</div>
<div class="ContentPasted0">real world, or with users they are supposed to support, must continuously</div>
<div class="ContentPasted0">make decisions based on sensor data, user input, knowledge they have</div>
<div class="ContentPasted0">acquired during runtime as well as knowledge provided during design-time.</div>
<div class="ContentPasted0">To make the behavior of such systems comprehensible, they need to be</div>
<div class="ContentPasted0">able to explain their decisions to the user or, after something has</div>
<div class="ContentPasted0">gone wrong, to an accident investigator.</div>
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<div class="ContentPasted0">While systems that use Machine Learning (ML) to interpret sensor</div>
<div class="ContentPasted0">data are very fast and usually quite accurate, their decisions are</div>
<div class="ContentPasted0">notoriously hard to explain, though huge efforts are currently being</div>
<div class="ContentPasted0">made to overcome this problem. In contrast, decisions made by</div>
<div class="ContentPasted0">reasoning about symbolically represented knowledge are in principle</div>
<div class="ContentPasted0">easy to explain. For example, if the knowledge is represented in (some</div>
<div class="ContentPasted0">fragment of) first-order logic, and a decision is made based on the result</div>
<div class="ContentPasted0">of a first-order reasoning process, then one can in principle use a formal</div>
<div class="ContentPasted0">proof in an appropriate calculus to explain a positive reasoning result,</div>
<div class="ContentPasted0">and a counter-model to explain a negative one. In practice, however, things</div>
<div class="ContentPasted0">are not so easy also in the symbolic KR setting. For example, proofs and</div>
<div class="ContentPasted0">counter-models may be very large, and thus it may be hard to comprehend</div>
<div class="ContentPasted0">why they demonstrate a positive or negative reasoning result, in particular</div>
<div class="ContentPasted0">for users that are not experts in logic. Thus, to leverage explainability as</div>
<div class="ContentPasted0">an advantage of symbolic KR over ML-based approaches, one needs to ensure</div>
<div class="ContentPasted0">that explanations can really be given in a way that is comprehensible to</div>
<div class="ContentPasted0">different classes of users (from knowledge engineers to laypersons).</div>
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<div class="ContentPasted0">The problem of explaining why a consequence does or does not follow from a</div>
<div class="ContentPasted0">given set of axioms has been considered for full first-order theorem proving</div>
<div class="ContentPasted0">since at least 40 years, but there usually with mathematicians as users in</div>
<div class="ContentPasted0">mind. In knowledge representation and reasoning, efforts in this direction</div>
<div class="ContentPasted0">are more recent, and were usually restricted to sub-areas of KR such as AI</div>
<div class="ContentPasted0">planning and description logics. The purpose of this workshop is to bring</div>
<div class="ContentPasted0">together researchers from different sub-areas of KR and automated deduction</div>
<div class="ContentPasted0">that are working on explainability in their respective fields, with the goal</div>
<div class="ContentPasted0">of exchanging experiences and approaches.</div>
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<div class="ContentPasted0">Topics of Interest</div>
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<div class="ContentPasted0">A non-exhaustive list of areas to be covered by the workshop are the following:</div>
<div class="ContentPasted0">* AI planning</div>
<div class="ContentPasted0">* Answer set programming</div>
<div class="ContentPasted0">* Argumentation frameworks</div>
<div class="ContentPasted0">* Automated reasoning</div>
<div class="ContentPasted0">* Causal reasoning</div>
<div class="ContentPasted0">* Constraint programming</div>
<div class="ContentPasted0">* Description logics</div>
<div class="ContentPasted0">* Non-monotonic reasoning</div>
<div class="ContentPasted0">* Probabilistic representation and reasoning</div>
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<div class="ContentPasted0">IMPORTANT DATES</div>
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<div class="ContentPasted0">Paper submission deadline: May 31, 2023</div>
<div class="ContentPasted0">Notification: July 4, 2022</div>
<div class="ContentPasted0">Workshop date: September 2, 2023</div>
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<div class="ContentPasted0">AUTHOR GUIDELINES AND SUBMISSION INFORMATION</div>
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<div class="ContentPasted0">We invite extended abstracts of 2-5 pages on topics related to explanation</div>
<div class="ContentPasted0">in logic-based KR. The papers should be formatted in Springer LNCS Style</div>
<div class="ContentPasted0">and can be submitted via EasyChair to the KR 2023 special track</div>
<div class="ContentPasted0">"Workshop on Explainable Logic-Based Knowledge Representation"</div>
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<div class="ContentPasted0"> https://easychair.org/conferences/?conf=kr2023</div>
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<div class="ContentPasted0">Since the workshop will only have informal proceedings and the main purpose is</div>
<div class="ContentPasted0">to exchange results, we welcome not only papers covering unpublished results,</div>
but also previous publications that fall within the scope of the workshop.<br>
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