[DL] CfP: The 3rd Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2022)

[Potyka, Nico]

[Apologies if you receive multiple copies]

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CALL FOR PAPERS
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The 3rd Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2022)
will be held in Haifa, Israel, on July 31, 2022, see

  https://sites.google.com/view/xlokr2022

It will be co-located with KR 2022 (https://kr2022.cs.tu-dortmund.de/)
at FLoC 2022 (https://www.floc2022.org/).


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Description
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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.


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Topics of Interest
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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


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IMPORTANT DATES
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Abstract submission deadline: May 2, 2022
Paper submission deadline: May 9, 2022
Notification: June 9, 2022
Workshop date: July 31, 2022


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AUTHOR GUIDELINES AND SUBMISSION INFORMATION
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We invite 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 can
be submitted via EasyChair:

  https://easychair.org/my/conference?conf=xlokr2022

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.
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