Proceedings

Generalized Safe Conditional Syntax Splitting of Belief Bases

Lars-Phillip Spiegel, Jonas Haldimann, Jesse Heyninck, Gabriele Kern-Isberner, Christoph Beierle

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4678-4686.

https://doi.org/10.24963/ijcai.2025/521

Keywords

  • Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning

Abstract

Splitting techniques in knowledge representation

help focus on relevant parts of a belief base and

reduce the complexity of reasoning generally. In

this paper, we propose a generalization of safe conditional syntax splittings that broadens the applicability of splitting postulates for inductive inference from belief bases. In contrast to safe conditional syntax splitting, our generalized notion supports syntax splittings of a belief base ∆ where the

subbases of ∆ may share atoms and nontrivial conditionals. We illustrate how this new notion overcomes limitations of previous splitting concepts,

and we identify genuine splittings, separating them

from simple splittings that do not provide benefits

for inductive inference from ∆. We introduce adjusted inference postulates based on our generalization of conditional syntax splitting. We evaluate

several inductive inference operators with respect

to these postulates, and show that generalized safe

conditional syntax splitting is a strictly stronger requirement for inductive inference operators, covering more syntax splitting applications.

Lars-Phillip Spiegel, Jonas Haldimann, Jesse Heyninck, Gabriele Kern-Isberner, and Christoph Beierle. 2025. Generalized Safe Conditional Syntax Splitting of Belief Bases. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4678–4686. https://doi.org/10.24963/ijcai.2025/521
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards