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].
Relevance in Belief Update
Authors: Theofanis Aravanis
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we show that there exist KM update operators that yield the same counter-intuitive results as any AGM revision operator. Against this non-satisfactory background, we prove that a translation of Parikh s relevance-sensitive axiom (P), in the realm of belief update, suffices to block this liberal behaviour of KM update operators. It is shown, both axiomatically and semantically, that axiom (P) for belief update, essentially, encodes a type of relevance that acts at the possible-worlds level, in the context of which each possible world is locally modified, in the light of new information. Interestingly, relevance at the possible-worlds level is shown to be equivalent to a form of relevance that acts at the sentential level, by considering the building blocks of relevance to be the sentences of the language. Furthermore, we concretely demonstrate that Parikh s notion of relevance in belief update can be regarded as (at least a partial) solution to the frame, ramification and qualification problems, encountered in dynamically-changing worlds. Last but not least, a whole new class of well-behaved, relevance-sensitive KM update operators is introduced, which generalize Forbus update operator and are perfectly-suited for real-world implementations. |
| Researcher Affiliation | Academia | Theofanis I. Aravanis EMAIL Department of Business Administration School of Economics & Business University of Patras Patras 265 00, Greece |
| Pseudocode | No | No pseudocode or algorithm blocks are provided. The paper focuses on axiomatic and semantic characterizations, theorems, and definitions within logic. |
| Open Source Code | No | The paper does not provide any statement regarding the release of open-source code for the methodology or operators described. There are no links to repositories or mentions of code in supplementary materials. |
| Open Datasets | No | The paper primarily uses conceptual examples like the 'book/magazine example' and a 'simple electric circuit' to illustrate its theoretical points. It does not utilize or refer to any publicly available datasets for experimental evaluation. |
| Dataset Splits | No | No specific datasets are used for empirical evaluation, hence no information regarding dataset splits is provided. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not describe any experimental setup that would involve specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or versions required to implement or replicate the theoretical framework. It operates within a propositional logic framework. |
| Experiment Setup | No | The paper is theoretical in nature and does not describe any empirical experiments or their setup, such as hyperparameters or training configurations. |