BDDs Strike Back (in AI Planning)
Authors: Stefan Edelkamp, Peter Kissmann, Alvaro Torralba
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The cost-optimal track of the international planning competition in 2014 has seen an unexpected outcome. ... In this paper we review the outcome of the competition, briefly looking into the internals of the competing systems. Figure 1: IPC-2014 Results, Sequential Optimal Track. |
| Researcher Affiliation | Academia | Stefan Edelkamp Institute of Artificial Intelligence University of Bremen edelkamp@tzi.de Peter Kissmann and Alvaro Torralba Foundations of Artificial Intelligence Saarland University, Saarbr ucken {kissmann,torralba}@cs.uni-saarland.de |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper reviews existing planning systems but does not provide any statement or link for the open-source code of the methodology or analysis presented in this paper. |
| Open Datasets | No | The paper mentions the International Planning Competition (IPC) 2014 and the number of problems solved (151 of 280), but does not provide concrete access information (e.g., specific link, DOI, or formal citation for a publicly available dataset) for the problems themselves in the context of training data. |
| Dataset Splits | No | The paper discusses the International Planning Competition (IPC) problems but does not specify any training, validation, or test dataset splits or their sizes. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments or analysis were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies or their version numbers (e.g., libraries, solvers) needed to replicate any analysis in this paper were mentioned. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, such as hyperparameter values, training configurations, or system-level settings. |