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].
Belief Update for Proper Epistemic Knowledge Bases
Authors: Tim Miller, Christian Muise
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by ο¬rst erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update. |
| Researcher Affiliation | Academia | Tim Miller and Christian Muise University of Melbourne, Melbourne, Australia MIT CSAIL, Massachusetts, USA EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Belief erasure |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper that does not involve experimental evaluation on datasets. Therefore, no information on public datasets or their availability is provided. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |