Decreasing Uncertainty in Planning with State Prediction
Authors: Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sandor Szedmak, Justus Piater
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions. ... In Section 4 we present the experimental results and we conclude in Section 5. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Innsbruck, Austria 2Department of Computer Science, King s College London, United Kingdom 3Department of Computer Science, Aalto University, Finland |
| Pseudocode | No | The paper includes descriptions of processes and mathematical formulations (e.g., equations, definitions), but it does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The system together with all domains and test scripts is available and can be found at github.com/Senka2112/IJCAI2017. |
| Open Datasets | No | The paper states: "With this framework we were able to generate randomised problem instances from four domains..." and cites papers for these domains. However, it does not provide concrete access (link, citation for dataset, etc.) to the specific *generated instances* used for training, only the general domains from which they were created. |
| Dataset Splits | Yes | To examine the reproducibility of prediction problems we randomly generated 10 states for each combination of the percentage of knowledge and problem size utilizing 10-cross-fold-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions several software components used, such as "ROSPlan", "M3VR", "CLG", "POPF", and "VAL", but it does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | Yes | The problem size is varied by increasing the number of objects from 5 to 100 by an increment of 5. The knowledge is varied by generating complete states and removing literals at random. Percentages of knowledge used in tests are 0.5%, 1%, 2%, 3%, 5%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%. ... with a time limit of 1800 [s]. ... All of the problems involve 20 objects. The number of goals was varied from 2 to 6. |