Planning under Uncertainty and Temporally Extended Goals
Authors: Alberto Camacho
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. acamacho@cs.toronto.edu [...] The contributions presented in this paper are joint work with (in alphabetical order) Jorge Baier (jabaier@ing.puc.cl), Sheila Mc Ilraith (sheila@cs.toronto.edu) (supervisor), Christian Muise (cjmuise@mit.edu), and Eleni Triantafillou (eleni@cs.toronto.edu). |
| 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. |