[PlanetKR] [CfP] KR 2026 Special track: KR meets Machine Learning and Explanation

[Nico Potyka]

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KR 2026 Special track: KR meets Machine Learning and Explanation
Call for Papers
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23rd International Conference on Principles of Knowledge Representation and Reasoning
https://kr.org/KR2026/

Machine learning (ML) has seen groundbreaking advancements across countless tasks in recent years, not least with the ongoing development and widespread deployment of large language models (LLMs). Concurrently, with this increase in both power and scope, it has become increasingly important that AI models are designed to be supplemented with explanations such that their outputs can be assessed, understood and modified if necessary. Meanwhile, the field of KR presents an excellent repertoire of technologies for leveraging knowledge in both ML and explanation pipelines. This special track thus aims to focus on the synergistic interactions between KR and these complementary fields of ML and explanation.

We welcome contributions that extend the state-of-the-art at the intersection of KR with either of the fields mentioned above. With regards to explanation, contributions that use KR in the explanation of AI models, or that explain numeric and/or symbolic models themselves, are welcomed. From the ML side, these may also include the use of KR methods for solving ML challenges, the use of ML methods for solving KR challenges or the integration of learning and reasoning towards better modelling, solving or explaining in different tasks. Papers focusing on evaluation protocols and benchmarking of these hybrid solutions will be also welcome.

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Important Dates
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Submission of title and abstract: February 12, 2026
Paper submission deadline: February 19, 2026
Author response period: March 24-28, 2026
Notification of acceptance: April 13, 2026
Camera-ready due: May 3, 2026

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Topics of Interest
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We welcome papers on a wide range of topics where KR is a key component, 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
KR, ML and reasoning in the computation, synthesis, analysis, verification, reuse, and repair of plans
Logic-based, logical and relational learning algorithms
ML-driven reasoning algorithms
Neural-symbolic learning
Statistical relational learning and KR
Symbolic reinforcement learning
The use of KR techniques for supplementing or evaluating LLMs
LLMs for supporting KR-driven methods
Knowledge-driven natural language understanding and dialogue
Learning symbolic abstractions from unstructured data
Expressive power of learning representations and explanations
Knowledge-driven decision making and explanations
Combining discrete and continuous, quantitative and qualitative, logical and probabilistic representations and reasoning methods (e.g., task planning with motion planning) with explanations
Architectures that combine data-driven techniques and formal reasoning
KR-driven Explainable AI
Interpretable ML models intertwined with KR
Combining KR and ML for enhanced explainability
Theoretical frameworks for explainability within KR and logic-based systems
Explainability in dynamic and temporal knowledge representation
Scalable approaches for real-time explainable reasoning using KR and ML
Evaluation protocols, metrics, and benchmarks for assessing the quality and clarity of explanations
Interactive and adaptive explanation frameworks using ML and KR
Personalisation of explanations through contextual KR, ML and user feedback
Identifying and mitigating systemic biases in AI explanations through KR
Hands-on tools and open-source libraries for explanations in real-world settings

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Types of Submissions
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Submissions to the special track can be either of the following types of technical contributions:

Long papers (9 pages excluding references)
Short papers (4 pages excluding references)

Both kinds of papers must be prepared and submitted according to the author guidelines on the submission page before the deadline (more information about submissions will be available in due course at https://kr.org/KR2026/submission.html). These papers must fall into the intersection of KR and either ML or explanation, or both. Papers not meeting this criterion will be identified before the actual reviewing process and will be desk-rejected.

Selected authors will be given the option to showcase their work in the Demo Track alongside their regular presentation slot in a session of the track.

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Inquiries
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Inquiries should be sent by email to kr26-mlx-chairs at lirmm.fr and will be handled by the KR meets Machine Learning and Explanation Track chairs:

Claudia d’Amato, University of Bari, Italy
Ute Schmid, University of Bamberg, Germany
Antonio Rago, King’s College London, UK
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