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].
Belief Revision Operators with Varying Attitudes Towards Initial Beliefs
Authors: Adrian Haret, Stefan Woltran
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on axiomatizations, representation results, and characterizations of belief revision operators, providing theorems and proofs. It constructs concrete operators based on theoretical ingredients (distance and aggregation functions) and analyzes their satisfaction of the introduced postulates (Table 2). There is no mention of empirical studies, datasets, or performance metrics. |
| Researcher Affiliation | Academia | 1Institute of Logic and Computation, TU Wien, Austria EMAIL |
| Pseudocode | No | The paper contains formal definitions, theorems, proofs, and examples, but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not describe a software implementation or provide links to any source code. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or empirical evaluation. Examples are used for illustration, not for training or testing models. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation or any empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training settings. |