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

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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)

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