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].
Symbolic Domain Predictive Control
Authors: Johannes Lรถhr, Martin Wehrle, Maria Fox, Bernhard Nebel
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the symbolic DPC approach in a orbital manoeuvre domain similar to the domain used by L ohr et al. (2013). The simulation part consists of three steps. First a symbolic reachability analysis is performed using the domain model and greedy search. Once we have found a plan we generate the continuous input signal. Finally we perform a numeric time simulation to obtain the continuous trajectory. The results are shown in Figure 7. On the left hand side the trajectory of the numerical time simulation is shown for each planning problem. The computational effort is shown in Table 3 in terms of explored nodes. |
| Researcher Affiliation | Academia | Johannes L ohr University of Freiburg Germany EMAIL Martin Wehrle University of Basel Switzerland EMAIL Maria Fox King s College London United Kingdom EMAIL Bernhard Nebel University of Freiburg Germany EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes a 'Case Study' where initial states for simulations are randomly initialized ('We randomly initialize a set of simulations with an initial position error...'). It does not use or provide access information for a public, established dataset. |
| Dataset Splits | No | The paper describes a simulation-based approach for control problems. It does not mention specific training, validation, or test dataset splits in the context of machine learning experiments. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments, such as GPU or CPU models, memory, or other specific computer specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or solvers used for implementation or experiments. |
| Experiment Setup | Yes | We consider a medium sized satellite of 1000 kg mass. Using thruster pulsing the provided force can be scaled within a continuous input spectrum from umin = 2.24 N to umax = 10 N. The duration of each action is chosen as ฮด = 50 seconds. Errors of the velocity are stronger weighted compared to the position errors by using the diagonal weighting matrix W with elements W11 = 1, W22 = 1, W33 = 10, W44 = 10 to obtain the heuristic value. |