Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Belief Change and Non-Monotonic Reasoning Sans Compactness
Authors: Jandson S. Ribeiro, Abhaya Nayak, Renata Wassermann3019-3026
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,284 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,546 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 paper we investigate the impact of such relaxation on non-monotonic logics instead. In particular, we show that, when compactness is not guaranteed, while the bridge from the AGM paradigm of belief change to expectation logics remains unaffected, the return trip from expectation logics to AGM paradigm is no longer guaranteed. We ο¬nally explore the conditions under which such guarantee can be given. We sketch proof of selected results in this paper; others will be provided in a planned future publication. |
| Researcher Affiliation | Academia | Jandson S. Ribeiro Macquarie University, Australia University of S ao Paulo, Brazil EMAIL EMAIL Abhaya Nayak Macquarie University, Australia EMAIL Renata Wassermann University of S ao Paulo, Brazil EMAIL |
| Pseudocode | No | The paper focuses on theoretical proofs and logical derivations, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | This is a theoretical paper; therefore, no source code for a methodology is mentioned or provided. |
| Open Datasets | No | This is a theoretical paper and does not use or mention any datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve datasets or data splitting for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not discuss computational experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not describe any specific software implementations or dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments, so there are no experimental setup details like hyperparameters or training configurations. |