KR&ML@KR 2022

 CALL FOR PAPERS
Special Session on KR and Machine Learning
19th International Conference on Principles of Knowledge Representation and Reasoning,
  KR 2022 — July 31 - August 5, 2022, Haifa, Israel (within FLoC 2022)
https://kr.org/KR2022/cfp_special_session_kr_and_machine_learning.php
Retrieved: 31 August 2026

Call for Papers: Special Session on KR and Machine Learning
The last few years have witnessed a growing interest 
in AI methods that combine aspects of Machine Learning (ML) 
with insights and methods from the field of Knowledge Representation 
and Reasoning (KR). This trend is essentially motivated 
by the clear complementarity of ML and KR. 
For instance, the popularity and success of ML based systems 
has put issues such as explainability, bias and fairness 
firmly in the spotlight, and addressing these issues 
naturally leads to systems in which symbolic 
(or at least interpretable) representations play a more central role. 
On the other hand, ML also offers solutions for long-standing challenges 
in the field of KR, for instance related to efficient, 
noise-tolerant and ampliative inference, knowledge acquisition, 
and the limitations of symbolic representations. 
The synergy between ML and KR has the potential to lead to 
new advancements in fundamental AI 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 third time, KR2022 will host a special session 
on "Knowledge Representation and Machine Learning", 
which aims at providing researchers and practitioners 
with a dedicated forum for the discussion of 
new ideas and research results at the intersection 
of these two fields. This special session will provide 
participants with the opportunity to make meaningful 
connections and develop a shared understanding of the challenges 
involved in developing innovative AI solutions that rely 
on a combination of insights and methods from ML and KR.
Submission Guidelines and Evaluation Criteria
The Special Session on KR and Machine Learning will allow contributions of both 
regular papers (9 pages)
and 
short papers (4 pages)
, excluding references, prepared and submitted according to the authors guidelines 
in the 
submission page
.
The special session 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 originality, 
soundness, relevance and significance of the technical contribution, 
as well as the overall presentation quality.
Special Session on KR & Machine Learning Chairs
Fabio Cozman
University of Sao Paulo, Brazil
Steven Schockaert
Cardiff University, UK
Important Dates
Submission of title and abstract: 
February 2, 2022
Paper submission deadline: 
February 9, 2022
February 11, 2022
(strict deadline)
Author response period: 
March 29-31, 2022
Author notification: 
April 15, 2022
Camera-ready papers: 
May 7, 2022
Conference: 
July 31 - August 5, 2022
Expected Contributions
The Special Session on KR and ML at KR2022 invites 
submissions of papers that combine aspects of KR and ML research, 
including the use of KR methods for solving ML challenges 
(e.g. knowledge-guided or explainable learning), the use of ML methods 
for solving KR challenges (e.g. efficient inference, knowledge base completion), 
the integration of learning and reasoning, 
and the the application of combined KR and ML approaches to solve real-world problems.
We welcome papers on a wide range of topics, including but not limited to:
Learning symbolic knowledge, such as ontologies and knowledge graphs, action theories, commonsense knowledge, spatial and temporal theories, preference models and causal models
Logic-based, logical and relational learning algorithms
Machine-learning driven reasoning algorithms
Neural-symbolic learning
Statistical relational learning
Multi-agent learning
Symbolic reinforcement learning
Learning symbolic abstractions from unstructured data
Explainable AI
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
Architectures that combine data-driven techniques and formal reasoning