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..
Planning under Uncertainty and Temporally Extended Goals
Authors: Alberto Camacho
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Prob PRP has two important merits. First, it overcomes scaling difficulties that previous o ine algorithms experienced. And second, it o ers superior optimality guarantees with respect to the previous state of the art in High Prob, the online planner RFF [Teichteil-K onigsbuch et al., 2010]. Despite being an o ine algorithm, Prob PRP outperforms RFF in general and solutions are of better quality. [...] Interestingly, PRP performance was better with NFA-based translations, with smaller policies and lower run-times than with AA-based translations. |
| Researcher Affiliation | Academia | Alberto Camacho Department of Computer Science University of Toronto. Canada. EMAIL [...] The contributions presented in this paper are joint work with (in alphabetical order) Jorge Baier (EMAIL), Sheila Mc Ilraith (EMAIL) (supervisor), Christian Muise (EMAIL), and Eleni Triantafillou (EMAIL). |
| Pseudocode | No | The paper describes algorithms and techniques verbally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper discusses planning problems and models (FOND, probabilistic planning) and refers to existing state-of-the-art planners (PRP, RFF) but does not mention or provide access information for any specific public datasets used for training or evaluation in the traditional sense of empirical data. |
| Dataset Splits | No | The paper describes models and results related to planning problems but does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'state-of-the-art FOND planner PRP [Muise et al., 2012]' but does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, solvers). |
| Experiment Setup | No | The paper describes the proposed methods and their general characteristics but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |