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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Observations on Darwiche and Pearl's Approach for Iterated Belief Revision
Authors: Theofanis Aravanis, Pavlos Peppas, Mary-Anne Williams
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,922 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,032 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we make further observations on the DP approach. In particular: ... The aforementioned incompatibility results imply significant corollaries for existing belief-change proposals. Parametrized Difference revision operators [Peppas and Williams, 2016; 2018], and their specialization Dalal's operator [Dalal, 1988], are incompatible with the DP approach. Moreover, the Independence postulate [Jin and Thielscher, 2007] (that remedies problems with the DP postulates) and the well-known Spohn's conditionalization [Spohn, 1988] (restricted to revision scenarios) are incompatible with axiom (P). We discuss the relation between belief states and total preorders over possible worlds, and prove that the one-to-one correspondence between them is not sufficient to cover all possible revision policies aligned with the DP postulates. This result implies that a preference ordering (over worlds) is an insufficient structure for representing a belief state. |
| Researcher Affiliation | Academia | Theofanis Aravanis1 , Pavlos Peppas1,2 and Mary-Anne Williams2 1University of Patras, Greece 2University of Technology Sydney, Australia EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, as it focuses on theoretical analysis rather than software implementation. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for validation or any empirical validation process. |
| Hardware Specification | No | The paper is theoretical and does not discuss specific hardware used for any experimental work. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |