Schematic Invariants by Reduction to Ground Invariants
Authors: Jussi Rintanen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have plotted the runtimes of the algorithm by Rintanen (2014) for the problems from the 2008, 2011 and 2014 competitions in Figure 3. Obviously, the number of actions (and state variables) is a decisive factor in the performance of the algorithm, with runtimes of each iteration approximately O(n3) in the size of the ground instance. A decrease in the number of ground actions and state variables decimates runtimes: at 250 ground actions they are never over 10 seconds. With 50 actions it is at most 3 seconds. Figure 4 demonstrates with applicable problem instances from the competitions 2008, 2011 and 2014 the runtime reduction obtained by grounding a temporal planning problem only partially according to Theorem 5 |
| Researcher Affiliation | Academia | Jussi Rintanen Department of Computer Science Aalto University, Helsinki, Finland Also affiliated with Griffith University, Brisbane, Australia, and the Helsinki Institute for Information Technology, Finland. |
| Pseudocode | Yes | Figure 1: Algorithm for invariants for classical planning; Figure 2: Algorithm for invariants for temporal planning |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In most of standard temporal planning benchmark problems the majority of schematic state variables and actions have at most one parameter of each type, sometimes 2, which by Theorem 5 leads to a low number of ground instances, well within the capabilities of the target algorithm (Rintanen 2014). Table 1 shows the numbers of ground instances for some of the problems from the 2014 planning competition. For each problem class we give the reduced number |Alim| of ground actions as justified by Theorem 5 and the highest number max |A| of ground actions of all instances in the class (after simplifications by the planner front-end). |
| Dataset Splits | No | The paper uses standard benchmark problems from planning competitions but does not explicitly describe the training, validation, or test splits. These details are typically part of the competition setup, but are not specified in the paper's text. |
| Hardware Specification | No | The paper mentions running algorithms and plotting runtimes but does not specify any hardware details like GPU/CPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or solvers). It references algorithms by Rintanen (2014) but no software stack. |
| Experiment Setup | Yes | Our novel idea is to devise hybrid algorithms, which perform the basic invariance tests with a ground method, but ground the actions and formulas only with respect to a small number of objects, roughly matching what takes place in schematic algorithms which partially instantiate schematic actions and formulas through unification and substitution operations. The challenge here is to keep the number of objects as small as possible to guarantee efficiency and scalability but not too small, to guarantee correctness and to identify as many and as strong invariants as possible. |