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..
A Simple Framework for Cognitive Planning
Authors: Jorge Luis Fernandez Davila, Dominique Longin, Emiliano Lorini, Frédéric Maris6331-6339
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a novel approach to cognitive planning... We encode the cognitive planning problem in an epistemic logic... We study a NP-fragment of the logic whose satisfiability problem is reduced to SAT. We provide complexity results for the cognitive planning problem. Moreover, we illustrate its potential for applications in human-machine interaction... We have studied both complexity of satisfiability for the logic and complexity of the cognitive planning problem. Our approach relies on SAT, given the NP-completeness of the satisfiability problem for the epistemic language we consider. |
| Researcher Affiliation | Academia | Jorge Luis Fernandez Davila,1 Dominique Longin,2 Emiliano Lorini,2 Fr ed eric Maris1 1IRIT, Toulouse University, France 2IRIT, CNRS, Toulouse University, France |
| Pseudocode | No | The paper presents a formal logical framework, definitions, and theorems, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We are currently implementing a cognitive planning algorithm using a SATsolver as well as the HMI scenario we presented in the paper. |
| Open Datasets | No | This is a theoretical paper that introduces a logical framework and studies its complexity. It does not use or reference any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, therefore, no training, validation, or test dataset splits are described. |
| Hardware Specification | No | The paper is theoretical and focuses on a logical framework and its complexity. It does not describe any specific hardware used for experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or version numbers. It mentions 'a SAT-solver' for future implementation, but without version details. |
| Experiment Setup | No | The paper is theoretical and focuses on a logical framework, definitions, and complexity results. It does not describe any experimental setup details, hyperparameters, or training configurations. |