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 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. |