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..
Reasoning about Probabilities in Unbounded First-Order Dynamical Domains
Authors: Vaishak Belle, Gerhard Lakemeyer
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we reconsider that model of belief, and propose a new logical variant that has much of the expressive power of the original, but goes beyond it in novel ways. In particular, by moving to a semantical account of a modal variant of the situation calculus based on possible worlds with unbounded domains and probabilistic distributions over them, we are able to capture the beliefs of a fully introspective knowledge base with uncertainty by way of an only-believing operator. The paper introduces the new logic and discusses key properties as well as examples that demonstrate how the beliefs of a knowledge base change as a result of noisy actions. |
| Researcher Affiliation | Academia | Vaishak Belle University of Edinburgh United Kingdom EMAIL and Gerhard Lakemeyer RWTH Aachen University Germany EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, thus no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets, thus no information about training/validation/test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |