Answer Update for Rule-Based Stream Reasoning

Authors: Harald Beck, Minh Dao-Tran, Thomas Eiter

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The paper focuses on designing and proving the correctness of an algorithm for incremental updates in stream reasoning, extending truth maintenance systems. While it discusses runtime complexity theoretically, it does not present empirical studies, dataset evaluations, performance metrics, or hypothesis validations from experiments. The examples provided are illustrative, not experimental results.
Researcher Affiliation Academia Institute of Information Systems, Vienna University of Technology Favoritenstraße 9-11, A-1040 Vienna, Austria {beck,dao,eiter}@kr.tuwien.ac.at
Pseudocode Yes Algorithm 1: Answer Update(t, D, var M)
Open Source Code No The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available.
Open Datasets No The paper uses illustrative examples (e.g., public transport scenario) but does not conduct experiments on a specific dataset, nor does it provide access information for any publicly available or open dataset.
Dataset Splits No The paper does not conduct experiments with datasets, so there is no mention of training/test/validation splits.
Hardware Specification No The paper is theoretical and does not describe any specific hardware used for experiments.
Software Dependencies No The paper mentions languages and systems like LARS, ASP, iclingo, and oclingo, but it does not specify any version numbers for software dependencies that would be required to replicate any experimental results or implementations.
Experiment Setup No The paper describes a theoretical algorithm and its properties. It does not include any experimental setup details, hyperparameters, or training configurations, as no empirical experiments are reported.