Belief Change and Non-Monotonic Reasoning Sans Compactness
Authors: Jandson S. Ribeiro, Abhaya Nayak, Renata Wassermann3019-3026
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 finally 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 jandson.santos-ribeiro-sant@hdr.mq.edu.au jandson@ime.usp.br Abhaya Nayak Macquarie University, Australia abhaya.nayak@mq.edu.au Renata Wassermann University of S ao Paulo, Brazil renata@ime.usp.br |
| 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. |