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Research Paper#Computational Logic, Complexity Theory🔬 ResearchAnalyzed: Jan 3, 2026 18:43

Complexity of Non-Classical Logics via Fragments

Published:Dec 29, 2025 14:47
1 min read
ArXiv

Analysis

This paper explores the computational complexity of non-classical logics (superintuitionistic and modal) by demonstrating polynomial-time reductions to simpler fragments. This is significant because it allows for the analysis of complex logical systems by studying their more manageable subsets. The findings provide new complexity bounds and insights into the limitations of these reductions, contributing to a deeper understanding of these logics.
Reference

“Propositional logics are usually polynomial-time reducible to their fragments with at most two variables (often to the one-variable or even variable-free fragments).”

PermalinkArXiv
Research Paper#Argumentation, Logic, AI🔬 ResearchAnalyzed: Jan 3, 2026 16:04

Encoding Higher-Order Argumentation Frameworks into Propositional Logic

Published:Dec 29, 2025 14:46
1 min read
ArXiv

Analysis

This paper addresses limitations in existing higher-order argumentation frameworks (HAFs) by introducing a new framework (HAFS) that allows for more flexible interactions (attacks and supports) and defines a suite of semantics, including 3-valued and fuzzy semantics. The core contribution is a normal encoding methodology to translate HAFS into propositional logic systems, enabling the use of lightweight solvers and uniform handling of uncertainty. This is significant because it bridges the gap between complex argumentation frameworks and more readily available computational tools.
Reference

“The paper proposes a higher-order argumentation framework with supports ($HAFS$), which explicitly allows attacks and supports to act as both targets and sources of interactions.”

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Research#llm📝 BlogAnalyzed: Dec 25, 2025 23:02

Learning from Professor Mokyr's "Useful Knowledge" Cycle and the Mission of AI Development for the 2025 Nobel Prize in Economics

Published:Dec 25, 2025 22:59
1 min read
Qiita AI

Analysis

This article highlights the importance of understanding the interplay between propositional knowledge (scientific principles) and prescriptive knowledge (technical recipes) in driving sustainable growth, as exemplified by Professor Joel Mokyr's work. It suggests that AI engineers should consider this dynamic when developing new technologies. The article likely delves into specific perspectives that engineers should adopt, emphasizing the need for a holistic approach that combines theoretical understanding with practical application. The focus on "useful knowledge" implies a call for AI development that is not just innovative but also addresses real-world problems and contributes to societal progress. The article's relevance lies in its potential to guide AI development towards more impactful and sustainable outcomes.
Reference

“"Propositional Knowledge: scientific principles" and "Prescriptive Knowledge: technical recipes"”

PermalinkQiita AI
Research#Logic🔬 ResearchAnalyzed: Jan 10, 2026 10:33

Cut-Elimination in Cyclic Proof Systems for Propositional Dynamic Logic

Published:Dec 17, 2025 04:38
1 min read
ArXiv

Analysis

This research explores a specific theoretical aspect of formal logic, which is crucial for the soundness and completeness of proof systems. The focus on cut-elimination within a cyclic proof system for propositional dynamic logic is a significant contribution to automated reasoning.
Reference

“A study of cut-elimination for a non-labelled cyclic proof system for propositional dynamic logics.”

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Research#Knowledge Graph🔬 ResearchAnalyzed: Jan 10, 2026 12:37

Knowledge Graph Framework for Industrial Standards: A Hierarchical and Propositional Approach

Published:Dec 9, 2025 09:26
1 min read
ArXiv

Analysis

This research explores a novel framework for structuring industrial standard documents using knowledge graphs, offering a potentially more efficient and accessible way to manage complex regulatory information. The focus on hierarchical and propositional structuring suggests a rigorous approach to semantic understanding and information retrieval.
Reference

“The article is sourced from ArXiv, suggesting peer-review may not be complete.”

PermalinkArXiv