Reasoning about Probabilities in Unbounded First-Order Dynamical Domains

Authors: Vaishak Belle, Gerhard Lakemeyer

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 vaishak@ed.ac.uk and Gerhard Lakemeyer RWTH Aachen University Germany gerhard@cs.rwth-aachen.de
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.