Strong Fully Observable Non-Deterministic Planning with LTL and LTLf Goals
Authors: Alberto Camacho, Sheila A. McIlraith
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithms have been implemented and evaluated empirically. Experiments demonstrate the effectiveness of our approach to computing strong solutions. They also suggest that assuming fairness and computing strong-cyclic solutions via existing approaches can ease the burden of computation. We implemented and evaluated the performance of our approaches in a selection of domains with LTL and LTLf goals. (Abstract, Section 7: Experiments) |
| Researcher Affiliation | Academia | Alberto Camacho and Sheila A. Mc Ilraith Department of Computer Science, University of Toronto, Toronto, Canada Vector Institute, Toronto, Canada {acamacho, sheila}@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1 Strong solutions to LTL FOND planning; Algorithm 2 Proof unsolvability in LTL FOND planning |
| Open Source Code | No | The paper states: "Our compilations are implemented in Python, and take (and produce) PDDL files." but does not provide any explicit statement about releasing the code for the described methodology or a link to a repository. |
| Open Datasets | No | The paper mentions using "Clerk, Lift, and Waldo domains from [Patrizi et al., 2013]", but it does not provide concrete access information (e.g., a specific link, DOI, or explicit statement of public availability) for these experimental domains/datasets. |
| Dataset Splits | No | The paper focuses on planning domains and does not describe experiments involving training, validation, or test dataset splits in the typical machine learning sense. |
| Hardware Specification | Yes | Our experiments ran in Ubuntu machines with an Intel(R) Xeon(R) 2.30GHz CPU. Runtime was limited to 30 minutes, and memory usage never exceeded 1GB. |
| Software Dependencies | No | The paper mentions using 'Spot', 'my ND planner', and 'PRP planner' but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper describes the planning domains and the use of off-the-shelf planners but does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or optimizer settings, which are typically found in machine learning contexts. |