Configuration Planning with Temporal Constraints
Authors: Uwe Kckemann, Lars Karlsson
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare these approaches in terms of the time it takes to solve problems and the quality of the solutions they provide. |
| Researcher Affiliation | Academia | Uwe K ockemann and Lars Karlsson Center for Applied Autonomous Sensor Systems, Orebro University, Sweden |
| Pseudocode | Yes | Algorithm 1 shows the general approach we use to solve the constraint-based planning problem. |
| Open Source Code | Yes | The implementation is available open source at spiderplan.org. |
| Open Datasets | Yes | All domain, problem, and planner definitions used in this paper can be found online (spiderplan.org). |
| Dataset Splits | No | The paper describes generating and solving problems but does not specify standard training, validation, or test dataset splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Mini Zinc' and 'Prolog solver YAP' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Parameters for domain generation are the number of sensors (20, 30, 40), information types (60, 90, 120), information links (100, 150, 200), objects (20), locations (10), sensor configurations (5), information cost (uniformly random between 0 and 100), goal start time (uniformly random between 100 and 100000), goal minimum duration (uniformly random between 50 and 100), required targeting conditions (2,3,4), required location conditions (2,3,4), required configuration conditions (2,3,4). |