Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Full Characterization of Parikh's Relevance-Sensitive Axiom for Belief Revision
Authors: Theofanis Aravanis, Pavlos Peppas, Mary-Anne Williams
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this article, the epistemic-entrenchment and partial-meet characterizations of Parikh s relevance-sensitive axiom for belief revision, known as axiom (P), are provided. In short, axiom (P) states that, if a belief set K can be divided into two disjoint compartments, and the new information ϕ relates only to the first compartment, then the revision of K by ϕ should not affect the second compartment. Accordingly, we identify the subclass of epistemic-entrenchment and that of selection-function preorders, inducing AGM revision functions that satisfy axiom (P). Hence, together with the faithful-preorders characterization of (P) that has already been provided, Parikh s axiom is fully characterized in terms of all popular constructive models of Belief Revision. |
| Researcher Affiliation | Academia | Theofanis I. Aravanis EMAIL Pavlos Peppas EMAIL Department of Business Administration University of Patras Patras 265 00, Greece Mary-Anne Williams EMAIL Centre for Artificial Intelligence FEIT, University of Technology Sydney NSW 2007, Australia |
| Pseudocode | No | The paper focuses on formal characterizations, definitions, and theorems related to belief revision axioms. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes theoretical characterizations and formal models in belief revision. It does not mention the release of any source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not conduct experiments involving datasets. Therefore, it does not provide any information about open datasets. |
| Dataset Splits | No | This is a theoretical paper that does not conduct experiments involving datasets. Therefore, it does not provide any information about dataset splits. |
| Hardware Specification | No | This is a theoretical paper presenting formal characterizations of an axiom in belief revision. It does not describe any experimental setup or mention specific hardware used. |
| Software Dependencies | No | This is a theoretical paper focusing on logical frameworks and axioms. It does not mention any specific software or programming libraries with version numbers used for implementation or experiments. |
| Experiment Setup | No | This is a theoretical paper that provides formal characterizations and proofs. It does not describe any experimental setup, hyperparameters, or training configurations. |