-------- Forwarded Message -------- Subject: [WI] Call for Papers: Special Session on KR & Machine Learning(KR2021) Date: Sat, 9 Jan 2021 09:51:25 +0700 From: Hồng Thơm Đinh Thị thomdth@eaut.edu.vn Reply-To: Hồng Thơm Đinh Thị thomdth@eaut.edu.vn To: agents@cs.umbc.edu
Call for Papers: Special Session on KR & Machine Learning (KR2021)
18th Conference on Principles of Knowledge Representation and Reasoning (KR2021)
November 6-12, 2021, Hanoi, Vietnam
https://kr2021.kbsg.rwth-aachen.de 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 generalizations 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-fertilization 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
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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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Vaishak Belle (University of Edinburgh, UK)
Luc De Raedt (KU Leuven, Belgium)