Knowledge Tree
explanation
Facts (15)
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Call for Papers: KR meets Machine Learning and Explanation kr.org 4 facts
perspectiveThe field of Knowledge Representation (KR) provides a repertoire of technologies for leveraging knowledge in both Machine Learning (ML) and explanation pipelines.
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 welcomes papers focusing on evaluation protocols and benchmarking of hybrid solutions that combine Knowledge Representation (KR) with Machine Learning (ML) or explanation.
Moving Forward on the Problem of Consciousness - David Chalmers consc.net 3 facts
claimDavid Chalmers argues that his proposed theory of consciousness can provide a solution that goes beyond mere correlation to explanation, similar to how Newton's theory of gravitation explains macroscopic regularities.
perspectiveDavid Chalmers argues that asking 'why does the fundamental law hold' is a question that should not expect an answer, as fundamental laws are the stopping point of explanation.
claimIn physics, explanation eventually stops at fundamental laws of nature, which are not further explained.
Epistemology - Stanford Encyclopedia of Philosophy plato.stanford.edu Dec 14, 2005 2 facts
claimExplanatory coherentism posits that a subject is justified in believing a hypothesis (H) when that hypothesis provides the best explanation for the subject's perceptual experiences.
claimExplanatory coherentism faces a circularity problem if it attempts to define what makes one explanation better than another by using the concept of justification, as this would make the account uninformative.
Consciousness (Stanford Encyclopedia of Philosophy/Fall 2025 ... plato.stanford.edu Jun 18, 2004 1 fact
referenceM. Silberstein published 'Converging on emergence: consciousness, causation and explanation' in the Journal of Consciousness Studies in 2001.
KR 2026 : 23rd International Conference on Principles of ... - WikiCFP wikicfp.com 1 fact
claimThe 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026) covers research topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in knowledge representation, explanation, abduction and diagnosis, geometric, spatial, and temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation, automated reasoning, satisfiability and model counting, knowledge representation languages, logic programming, answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of knowledge representation, qualitative reasoning, reasoning about actions and change, action languages, reasoning about knowledge, beliefs, and other mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 1 fact
claimAn additional process may be required between the original explanation generated by an AI system and the explanation provided to the user, as the original output may be too technical for non-expert users.
Call for Papers: Main Track - KR 2026 kr.org 1 fact
claimThe KR 2026 conference accepts submissions on topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in KR, explanation/abduction/diagnosis, geometric/spatial/temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation/automated reasoning/satisfiability/model counting, knowledge representation languages, logic programming/answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of KR, qualitative reasoning, reasoning about actions and change/action languages, reasoning about knowledge/beliefs/mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 1 fact
claimThe interpretability of Neuro-Symbolic AI (NSAI) systems is assessed through three criteria: transparency (the clarity of internal mechanisms and decision processes), explanation (the ability to provide comprehensible justifications for predictions), and traceability (the capability to reconstruct the sequence of operations contributing to an outcome).
Hard Problem of Consciousness | Internet Encyclopedia of Philosophy iep.utm.edu 1 fact
quoteJoseph Levine states regarding the third possibility for why a deduction might fail: "precisely an admission that we don’t have an adequate explanation" (2001, 76).