Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
The concept 'machine learning' is a core component of the 'KR meets Machine Learning and Explanation' track, which explicitly focuses on the synergistic interactions and integration between these fields as described in [1], [2], and [3]. Furthermore, [4] mandates that all submissions to this track must involve the intersection of machine learning and knowledge representation.
Facts (5)
Sources
Call for Papers: KR meets Machine Learning and Explanation kr.org 5 facts
procedureThe KR 2026 special track 'KR meets Machine Learning and Explanation' mandates that all submissions must fall into the intersection of Knowledge Representation and either Machine Learning or explanation; papers that do not meet this criterion will be desk-rejected before the review process begins.
claimThe KR 2026 special track on 'KR meets Machine Learning and Explanation' aims to focus on the synergistic interactions between Knowledge Representation (KR) and the fields of Machine Learning (ML) and explanation.
claimThe KR 2026 special track invites contributions that use Knowledge Representation (KR) methods to solve Machine Learning (ML) challenges, use ML methods to solve KR challenges, or integrate learning and reasoning for better modeling, solving, or explaining tasks.
claimThe KR 2026 special track 'KR meets Machine Learning and Explanation' invites research on the intersection of Knowledge Representation and Machine Learning, specifically covering topics such as learning symbolic knowledge (ontologies, knowledge graphs, action theories), KR-driven plan computation, logic-based learning, neural-symbolic learning, statistical relational learning, symbolic reinforcement learning, and the mutual use of KR techniques and LLMs.
claimThe KR 2026 special track welcomes papers focusing on evaluation protocols and benchmarking of hybrid solutions that combine Knowledge Representation (KR) with Machine Learning (ML) or explanation.