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..
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
Authors: Masataro Asai, Alex Fukunaga
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Lat Plan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and Lights Out. |
| Researcher Affiliation | Academia | Masataro Asai, Alex Fukunaga Graduate School of Arts and Sciences The University of Tokyo |
| Pseudocode | No | The paper describes algorithmic steps but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Latplan code is available on Github. |
| Open Datasets | Yes | MNIST 8-puzzle is an image-based version of the 8-puzzle, where tiles contain hand-written digits (0-9) from the MNIST database (Le Cun et al. 1998). |
| Dataset Splits | No | The paper mentions training various components (SAE, AAE, AD, SD) but does not specify explicit training, validation, and test splits for the data used in training the models. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using "a modified version of Fast Downward (Helmert 2006)" but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | No | The paper describes the general training processes (e.g., minimizing reconstruction loss, annealing Gumbel-Softmax temperature) but does not provide specific hyperparameter values like learning rates, batch sizes, or explicit training schedules. |