<div dir="ltr"><p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif"><span style="font-family:Arial,sans-serif;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Call for Papers: Special Session on KR & Machine Learning
(KR2021)</span></p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">18th Conference on Principles of Knowledge Representation
and Reasoning (KR2021)</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">November 6-12, 2021, Hanoi, Vietnam</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif"><a href="https://kr2021.kbsg.rwth-aachen.de">https://kr2021.kbsg.rwth-aachen.de</a></p>
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<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Important Dates</p>
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<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Submission of title and abstract: March 24, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Paper submission deadline: March
31, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Author response period: May
24-26, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Notification: June
15, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Camera-ready papers: July
14, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Conference dates: November
6-12, 2021</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-----------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Description</p>
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<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Over the last two decades, Machine Learning (ML) has made
incredible progress </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">and become very effective at solving specific tasks while
being robust across</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">many experimental learning applications. Deep learning,
statistical (relational)</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">learning, reinforcement learning and logic-based and/or
probabilistic learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">are among the many ML approaches that are witnessing such
advancements. On the </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">other hand, Knowledge Representation and Reasoning (KR) has
continued to be at </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">the core of Artificial Intelligence (AI) research providing
solutions for </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">explicit declarative representation of knowledge and
knowledge-based inference, </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">which have theoretical and practical relevance in many
aspects of AI as well </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">as in new emerging fields outside AI. The synergy between
these two areas of AI</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">has the potential to lead to new advancements on the
foundations of AI that</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">offer novel insights into open fundamental challenges
including, but not limited</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">to, learning symbolic generalizations from raw (multi-modal)
data, using</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">knowledge to facilitate data-efficient learning, supporting
interpretability of</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">learned outcomes, federated multi-agent learning and
decision making.</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">This year, for the second time, KR2021 will host a special
session on "Knowledge</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Representation and Machine Learning". This special
session aims at providing</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">researchers and industrial practitioners with a dedicated
forum for presentation</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">and discussion of new ideas, research experience and
emerging results on topics</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">related to computational learning and symbolic knowledge
representation and</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">reasoning. This special session provides the opportunity for
fostering </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">meaningful connections between researchers from these two
main areas of AI and,</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">at the same time, offering the possibility to learn about
progress made on these</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">topics, share their own views and learn about approaches
that could lead to</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">effective cross-fertilization among research in ML and KR
and new innovative</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">solutions to key AI research challenges. </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">----------------------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Expected contributions</p>
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<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">The Special Session on KR and ML at KR2021 invites submissions
of papers across</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">KR and ML on advancements in one of these areas for the
purpose of addressing</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">open research challenges in the other, integration of
computational learning and</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">knowledge representation and reasoning, and the application
of combined KR and</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">ML approaches to solve real-world problems, including case
studies and</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">benchmarks. </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">We welcome papers on a wide range of topics, including but
not limited to:</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning ontologies and knowledge graphs</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning action theories</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning common-sense knowledge</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning spatial and temporal theories</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning preference models</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning causal models</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning tractable probabilistic models</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Probabilistic reasoning and learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Graphical models for knowledge representation and
reasoning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Reasoning and learning over knowledge graphs</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Logic-based learning algorithms</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Neural-symbolic learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Interplay between logic & neural and other learning
paradigms (e.g., logics</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">for reasoning about neural networks, embedding of logical
reasoning in neural</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">paradigms)</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Statistical relational learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Multi-agent learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Machine learning for efficient knowledge inference</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Symbolic reinforcement learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Learning symbolic abstractions from unstructured data</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Machine-learning-driven reasoning algorithms</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Explainable AI</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Transfer learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Multi-agent learning</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Expressive power of learning representations</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Knowledge-driven natural language understanding and
dialogue</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Knowledge-driven decision making</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Knowledge-driven intelligent systems for internet of
things and cybersecurity</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Application of knowledge-driven ML to question answering
and story</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">understanding </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">-- Application of knowledge-driven ML to Robotics</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">--------------------------------------------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Submission Guidelines and Evaluation Criteria</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">---------------------------------------------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">The special session emphasizes KR and ML, and welcomes
contributions that extend</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">the state of the art at the intersection of KR and ML.
Therefore, KR-only or </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">ML-only submissions will not be accepted for evaluation in
this special session. </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Submissions will be rigorously peer reviewed by PC members
who are active in KR</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">and ML. Submissions will be evaluated on the basis of the
overall quality of</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">their technical contribution, including criteria such as
originality, soundness,</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">relevance, significance, quality of presentation, and
understanding of the </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">state of the art.</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">In this special session, the selection process of the
highest quality papers</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">will apply the following criteria:</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">* Importance and novelty of using knowledge representation
and reasoning to</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">advance machine learning, or novelty of using machine
learning solutions to</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">advance knowledge representation and reasoning.</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">* Applicability of the proposed solutions in real-world.</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">* Reusability of datasets, case studies and benchmarks for
systems and/or</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">application papers.</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">* Proved theoretical or empirically demonstrated practical
advancement of the</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">proposed solution with respect to baseline pure KR or ML
approaches. </p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">Chairs</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif">------</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif"> Vaishak Belle
(University of Edinburgh, UK)</p>
<p class="MsoNormal" style="margin:0in 0in 8pt;line-height:107%;font-size:11pt;font-family:Calibri,sans-serif"> Luc De Raedt (KU
Leuven, Belgium)</p><div><br></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><img src="https://docs.google.com/uc?export=download&id=17ooA_tTVj1EVXDCIvdMdMkGaN1lW7_dF&revid=0BwScEBmBf4XuVm1tb0ZDTDR6elRiM0NIUlAvUUdRanNJK1FVPQ"></div><div>Hong Thom</div><div dir="ltr"><div style="margin:0px;padding:0px 0px 20px;width:1190px;color:rgb(34,34,34);font-family:Roboto,RobotoDraft,Helvetica,Arial,sans-serif;font-size:medium"><div><div style="margin:8px 0px 0px;padding:0px"><div><div dir="ltr"><font color="#888888"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><p style="margin:0cm 0cm 0.0001pt;background-image:initial;background-position:initial;background-repeat:initial"><b><font color="#0b5394">International Training and Cooperation </font></b><b style="font-size:12.8px"><font color="#0b5394">Institute</font></b></p><p style="margin:0cm 0cm 0.0001pt;background-image:initial;background-position:initial;background-repeat:initial"><b><font color="#0b5394">East Asia University of Technology</font></b></p><p style="margin:0cm 0cm 0.0001pt;background-image:initial;background-position:initial;background-repeat:initial"><font color="#0b5394"><b style="font-size:12.8px">Add: Polyco Group Building, Tran Huu Duc St, Nam Tu Liem Dist, Ha Noi</b><br></font></p><p style="margin:0cm 0cm 0.0001pt;background-image:initial;background-position:initial;background-repeat:initial"><b style="font-size:12.8px"><font color="#0b5394">Mobile: + 84 939411986</font></b></p><p style="margin:0cm 0cm 0.0001pt;background-image:initial;background-position:initial;background-repeat:initial"><span style="color:rgb(32,33,36)">Website:</span><a href="https://duhoc.eaut.edu.vn/#" target="_blank">https://duhoc.eaut.edu.vn/#</a></p></div></div></div></div></div></div></div></div></div></div></div></div></div></font></div></div></div></div></div></div><div dir="ltr">Email: <a href="mailto:esa@eaut.edu.vn" target="_blank">esa@eaut.edu.vn</a>; <a href="mailto:thomdth@eaut.edu.vn" target="_blank">thomdth@eaut.edu.vn</a>; <br></div><div dir="ltr"><br></div><div><br></div><div dir="ltr"><img src="https://docs.google.com/uc?export=download&id=1ogDsUq7lNj4EdZhxRrSLhlhzgKZVQcRR&revid=0BwScEBmBf4XuYmZiSzEvN2dEV0RESkRGNG5rWWVlVGkyN3U0PQ"><br><br></div></div></div></div></div></div>