Abstraction Heuristics for Symbolic Bidirectional Search
Authors: álvaro Torralba, Carlos Linares López, Daniel Borrajo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments on the optimal-track STRIPS planning instances from IPC 98 until IPC 14. All experiments were conducted on a cluster of Intel E5-2660 machines running at 2.20 GHz, with time (memory) cut-offs of 30 minutes (4 GB). Table 1 compares Sym BA with different abstraction strategies against the current state-of-the-art planner in symbolic search, bidirectional uniform-cost search (SB), and one state-of-the-art explicit-state search planner, METIS [Alkhazraji et al., 2014]. |
| Researcher Affiliation | Academia | Alvaro Torralba Saarland University Saarbr ucken, Germany torralba@cs.uni-saarland.de Carlos Linares L opez and Daniel Borrajo Universidad Carlos III de Madrid Madrid, Spain {carlos.linares,daniel.borrajo}@uc3m.es |
| Pseudocode | Yes | Algorithm 1: Sym BA Input: Planning problem: = h V, A, I, Gi Output: Cost-optimal plan or no plan |
| Open Source Code | No | The paper states 'Sym BA is implemented on top of Fast Downward [Helmert, 2006]' but does not provide a link or explicit statement about the open-sourcing of Sym BA's specific code. |
| Open Datasets | Yes | We run experiments on the optimal-track STRIPS planning instances from IPC 98 until IPC 14. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. It refers to 'optimal-track STRIPS planning instances from IPC 98 until IPC 14', which are standard competition benchmarks but no explicit split percentages or methods are described. |
| Hardware Specification | Yes | All experiments were conducted on a cluster of Intel E5-2660 machines running at 2.20 GHz, with time (memory) cut-offs of 30 minutes (4 GB). |
| Software Dependencies | No | The paper mentions 'Sym BA is implemented on top of Fast Downward [Helmert, 2006] and uses h2 in a precomputation step' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We consider a search feasible if the frontier has less than 10 million BDD nodes and each step takes less than 45 seconds, which are adequate values for the memory and time limits of our experiments. In our evaluation we focus on the simpler variant, PDBs. To select the pattern of the PDBs we follow a strategy previously used for symbolic perimeter abstractions [Torralba et al., 2013], which selects a variable ordering and relax variables one by one until the search can be continued. We use six different variable orderings. ipc1 uses cgr, gcl and rev. ipc2 uses the same PDB strategies plus a M&S strategy based on bisimulation with a limit of 10 000 abstract states. |