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 Revision and Progression of Knowledge Bases in the Epistemic Situation Calculus
Authors: Christoph Schwering, Gerhard Lakemeyer, Maurice Pagnucco
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we propose a novel framework for computing progression in the epistemic situation calculus. In particular, we model an agent’s preferential belief structure using conditional statements and provide a technique for updating these conditional statements as actions are performed and sensing information is received. Moreover, we show, by using the concepts of natural revision and only-believing, that the progression of a conditional knowledge base can be represented by only-believing the revised set of conditional statements. These results lay the foundations for feasible belief progression due to the unique-model property of only-believing. |
| Researcher Affiliation | Academia | Christoph Schwering RWTH Aachen University Aachen, Germany EMAIL Gerhard Lakemeyer RWTH Aachen University Aachen, Germany EMAIL Maurice Pagnucco University of New South Wales Sydney, Australia EMAIL |
| Pseudocode | No | The paper provides formal definitions and theorems but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release for the methodology described. |
| Open Datasets | No | The paper does not describe experiments using publicly available datasets, and therefore no concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper does not describe any experiments that would involve dataset splits. |
| Hardware Specification | No | The paper does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, as it focuses on theoretical contributions rather than implementation. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameter values or training configurations. |