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 [1].
Compilation Based Approaches to Probabilistic Planning — Thesis Summary
Authors: Ran Taig
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In most benchmarks, PFF s results were improved by our results. Results for this algorithm were good but for a limited, simple, set of benchmarks. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before. |
| Researcher Affiliation | Academia | Ran Taig Department of Computer Science Ben Gurion University of The Negev Beer-Sheva, Israel 84105 EMAIL |
| Pseudocode | No | The paper describes algorithmic ideas but does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper refers to 'benchmarks' and mentions 'PFF's results' but does not provide concrete access information (link, DOI, specific citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., exact percentages, sample counts, or methodology) needed to reproduce data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Metric FF' and 'off-the-shelf conformant planner' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes its compilation methods conceptually but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |