[DL] [CFP]**Special Session on Knowledge Representation and Machine Learning** at the, 18th Conference on Principles of Knowledge Representation and Reasoning (KR2021)

[Thanh Dinh]

Call for Papers

**Special Session on Knowledge Representation and Machine Learning** at the
18th Conference on Principles of Knowledge Representation and Reasoning 
(KR2021)


November 6-12, 2021, Hanoi, Vietnam


https://kr2021.kbsg.rwth-aachen.de


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Important Dates
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Submission of title and abstract:     March 24, 2021


Paper submission deadline:         March 31, 2021


Author response period:         May 24-26, 2021


Notification:                 June 15, 2021


Camera-ready papers:             July 14, 2021


Conference dates:             November 6-12, 2021


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Description

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Over the last two decades, Machine Learning (ML) has made incredible 
progress
and become very effective at solving specific tasks while being robust 
across
many experimental learning applications. Deep learning, statistical 
(relational)
learning, reinforcement learning and logic-based and/or probabilistic 
learning
are among the many ML approaches that are witnessing such advancements. 
On the
other hand, Knowledge Representation and Reasoning (KR) has continued to 
be at
the core of Artificial Intelligence (AI) research providing solutions for
explicit declarative representation of knowledge and knowledge-based 
inference,
which have theoretical and practical relevance in many aspects of AI as 
well
as in new emerging fields outside AI. The synergy between these two 
areas of AI
has the potential to lead to new advancements on the foundations of AI that
offer novel insights into open fundamental challenges including, but not 
limited
to, learning symbolic generalisations from raw (multi-modal) data, using
knowledge to facilitate data-efficient learning, supporting 
interpretability of
learned outcomes, federated multi-agent learning and decision making.



This year, for the second time, KR2021 will host a special session on 
"Knowledge
Representation and Machine Learning". This special session aims at providing
researchers and industrial practitioners with a dedicated forum for 
presentation
and discussion of new ideas, research experience and emerging results on 
topics
related to computational learning and symbolic knowledge representation and
reasoning. This special session provides the opportunity for fostering
meaningful connections between researchers from these two main areas of 
AI and,
at the same time, offering the possibility to learn about progress made 
on these
topics, share their own views and learn about approaches that could lead to
effective cross-fertilisation among research in ML and KR and new innovative
solutions to key AI research challenges.



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Expected contributions
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The Special Session on KR and ML at KR2021 invites submissions of papers 
across
KR and ML on advancements in one of these areas for the purpose of 
addressing
open research challenges in the other, integration of computational 
learning and
knowledge representation and reasoning, and the application of combined 
KR and
ML approaches to solve real-world problems, including case studies and
benchmarks.



We welcome papers on a wide range of topics, including but not limited to:



-- Learning ontologies and knowledge graphs


-- Learning action theories


-- Learning common-sense knowledge


-- Learning spatial and temporal theories


-- Learning preference models


-- Learning causal models


-- Learning tractable probabilistic models


-- Probabilistic reasoning and learning


-- Graphical models for knowledge representation and reasoning


-- Reasoning and learning over knowledge graphs


-- Logic-based learning algorithms


-- Neural-symbolic learning

-- Interplay between logic & neural and other learning paradigms (e.g., 
logics
for reasoning about neural networks, embedding of logical reasoning in 
neural
paradigms)


-- Statistical relational learning


-- Multi-agent learning


-- Machine learning for efficient knowledge inference


-- Symbolic reinforcement learning


-- Learning symbolic abstractions from unstructured data


-- Machine-learning-driven reasoning algorithms


-- Explainable AI


-- Transfer learning


-- Multi-agent learning


-- Expressive power of learning representations


-- Knowledge-driven natural language understanding and dialogue


-- Knowledge-driven decision making


-- Knowledge-driven intelligent systems for internet of things and 
cybersecurity


-- Application of knowledge-driven ML to question answering and story
understanding


-- Application of knowledge-driven ML to Robotics




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Submission Guidelines and Evaluation Criteria

---------------------------------------------



The special session emphasizes KR and ML, and welcomes contributions 
that extend
the state of the art at the intersection of KR and ML. Therefore, 
KR-only or
ML-only submissions will not be accepted for evaluation in this special 
session.



Submissions will be rigorously peer reviewed by PC members who are 
active in KR
and ML. Submissions will be evaluated on the basis of the overall quality of
their technical contribution, including criteria such as originality, 
soundness,
relevance, significance, quality of presentation, and understanding of the
state of the art.



In this special session, the selection process of the highest quality papers
will apply the following criteria:



* Importance and novelty of using knowledge representation and reasoning to
advance machine learning, or novelty of using machine learning solutions to
advance knowledge representation and reasoning.



* Applicability of the proposed solutions in real-world.



* Reusability of datasets, case studies and benchmarks for systems and/or
application papers.



* Proved theoretical or empirically demonstrated practical advancement 
of the
proposed solution with respect to baseline pure KR or ML approaches.


Details on formatting and submission can be found on the KR21 website.

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Remote Participation Due to the Covid-19 Pandemic

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We understand that the global public health situation may make it difficult
or impossible for some, if not all, participants to travel to Hanoi. For
this reason, we commit to allowing authors of accepted papers to present
virtually and will work hard to enable the best possible experience for
all conference participants.




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Chairs

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    Vaishak Belle (University of Edinburgh, UK)

    Luc De Raedt (KU Leuven, Belgium)