Sequencing Operator Counts

Authors: Toby O. Davies, Adrian R. Pearce, Peter J. Stuckey, Nir Lipovetzky

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our most interesting experimental result is that finding or refuting a sequence for an operator-count is most often empirically efficient, enabling a novel and promising approach to planning based on Logic-Based Benders Decomposition (LBBD). We experimentally confirmed that h can be computed using only this algorithm, and demonstrated that it outperforms the previous state-of-the-art in incremental lower bounding: h++.
Researcher Affiliation Academia Toby O. Davies, Adrian R. Pearce, Peter Stuckey National ICT Australia and The University of Melbourne Melbourne, Australia first.last@nicta.com.au Nir Lipovetzky Computing & Information Systems The University of Melbourne Melbourne, Australia nir.lipovetzky@unimelb.edu.au
Pseudocode No Our SAT model (ommited for brevity). The paper describes the steps in prose but does not provide structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access information (e.g., a link or explicit statement of release) for the source code of the methodology described.
Open Datasets No The paper mentions working with an instance of the 'gripper domain' and refers to it in Figure 1, but does not provide access information (such as a URL, DOI, or formal citation with author/year) for a publicly available dataset.
Dataset Splits No The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits).
Hardware Specification No The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory amounts) used for running its experiments.
Software Dependencies No The paper mentions types of solvers like 'Mixed Integer Programming' and 'Conflict-Directed Clause Learning SAT solvers' but does not specify exact version numbers for any software dependencies.
Experiment Setup No The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings.