On Creating Complementary Pattern Databases
Authors: Santiago Franco, Álvaro Torralba, Levi H. S. Lelis, Mike Barley
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we introduce a method that sequentially creates multiple PDBs which are later combined into a single heuristic function. At a given iteration, our method uses estimates of the A running time to create a PDB that complements the strengths of the PDBs created in previous iterations. We evaluate our algorithm using explicit and symbolic PDBs. Our results show that the heuristics produced by our approach are able to outperform existing schemes, and that our method is able to create PDBs that complement the strengths of other existing heuristics such as a symbolic perimeter heuristic. |
| Researcher Affiliation | Academia | 1 School of Computing and Engineering, University of Huddersfield, UK 2 Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany 3 Departamento de Inform atica, Universidade Federal de Vic osa, Brazil 4 Computer Science Department, Auckland University, New Zealand |
| Pseudocode | Yes | Algorithm 1 is a high-level overview of the search CPC performs in the pattern collection space. |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of its source code. |
| Open Datasets | Yes | We evaluate CPC on the STRIPS optimal benchmark suite distributed with the Fast Downward Planning System [Helmert, 2006]. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It evaluates on a benchmark suite of 1,667 planning tasks. |
| Hardware Specification | Yes | We use 2.67 GHz Linux sandybridge Xeon CPUs, and all planners we use are implemented within the Fast Downward planning system. |
| Software Dependencies | No | The paper mentions the "Fast Downward planning system" and specific implementations of PDBs, but it does not provide specific version numbers for all key software components (e.g., programming language, libraries, or exact Fast Downward version). |
| Experiment Setup | No | The paper mentions time and memory limits for experiments (1,800 seconds and 4 GBs of RAM) and some parameters for PDB construction (Smin, Smax, mutation rate implicitly by probability), but it does not provide detailed hyperparameters, optimizer settings, or other specific system-level training configurations. |