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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Belief Change in a Preferential Non-monotonic Framework
Authors: Giovanni Casini, Thomas Meyer
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we show that we can also integrate the two formalisms by studying belief change within a (preferential) non-monotonic framework. This integration relies heavily on the identification of the monotonic core of a non-monotonic framework. We consider belief change operators in a non-monotonic propositional setting with a view towards preserving consistency. These results can also be applied to the preservation of coherence an important notion within the field of logic-based ontologies. We show that the standard AGM approach to belief change can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results. |
| Researcher Affiliation | Academia | Giovanni Casini Universit e du Luxembourg Luxembourg EMAIL Thomas Meyer CAIR-CSIR University of Cape Town South Africa EMAIL |
| Pseudocode | No | The paper focuses on theoretical definitions, postulates, and theorems, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, so there is no mention of training data availability. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, so there is no mention of validation splits or processes. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments; therefore, it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not involve implementation details or computational experiments, so it does not list any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on formal definitions and proofs. It does not describe any practical experimental setups, hyperparameters, or training configurations. |