Belief Update without Compactness in Non-finitary Languages
Authors: Jandson S Ribeiro, Abhaya Nayak, Renata Wassermann
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main paradigms of belief change require the background logic to be Tarskian and finitary. We look at belief update when the underlying logic is not necessarily finitary. We show that in this case the classical construction for KM update does not capture all the rationality postulates for KM belief update. Indeed, this construction, being fully characterised by a subset of the KM update postulates, is weaker. We explore the reason behind this, and subsequently provide an alternative constructive accounts of belief update which is characterised by the full set of KM postulates in this more general framework. |
| Researcher Affiliation | Academia | Jandson S. Ribeiro1,2 , Abhaya Nayak1 and Renata Wassermann2 1 Macquarie University, Australia 2 University of S ao Paulo, Brazil |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets, therefore no information about public availability of datasets or their access details is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with datasets, so no information about training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |