[Event at CIG] Explainable Logic-Based Knowlede Representation - Call for Papers

[Bart Bogaerts]

[Apologies for cross-posting]

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/

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.


-- 

Prof. Bart Bogaerts
Artificial Intelligence Laboratory
Department of Computer Science
Vrije Universiteit Brussel
Pleinlaan 9, 1050 Brussels, 3rd floor
bart.bogaerts at vub.be