[fg-arc] KR2020 & ML - CFP

[Theofanis I. Aravanis]

Call for Papers
**Special Session on Knowledge Representation and Machine Learning**
at the 17th International Conference on Principles of Knowledge
Representation and Reasoning (KR2020)

September 12-18th, 2020
Rhodes, Greece

https://kr2020.inf.unibz.it/

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Important Dates
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Submission of title and abstract: 4 March 2020
Paper submission deadline: 11 March 2020
Author response period: 4--6 May 2020
Notification: 27 May 2020
Camera-ready papers: 24 June 2020
Conference dates: 12--18 September 2020  

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Description
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Over the last two decades Machine Learning (ML) has made incredible
advancements showing to be very effective in solving specific tasks and
robust in 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 unlock new
advancements on foundations of AI that offer new insights into open
fundamental challenges included, 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 first time, KR2020 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 KR2020 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
-- 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
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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. 

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Chairs
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Alessandra Russo (Imperial College London, UK) 
Guy Van den Broeck (UCLA, USA)
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